WO2008052057A2 - Method and system for providing analyte monitoring - Google Patents
Method and system for providing analyte monitoring Download PDFInfo
- Publication number
- WO2008052057A2 WO2008052057A2 PCT/US2007/082382 US2007082382W WO2008052057A2 WO 2008052057 A2 WO2008052057 A2 WO 2008052057A2 US 2007082382 W US2007082382 W US 2007082382W WO 2008052057 A2 WO2008052057 A2 WO 2008052057A2
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- signal
- processors
- dropout
- noise
- noise filtered
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
- A61B5/002—Monitoring the patient using a local or closed circuit, e.g. in a room or building
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
- A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14503—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14546—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring analytes not otherwise provided for, e.g. ions, cytochromes
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1468—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using chemical or electrochemical methods, e.g. by polarographic means
- A61B5/1473—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1486—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using enzyme electrodes, e.g. with immobilised oxidase
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
- A61B5/4839—Diagnosis combined with treatment in closed-loop systems or methods combined with drug delivery
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1468—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using chemical or electrochemical methods, e.g. by polarographic means
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
Definitions
- Analyte e.g., glucose monitoring systems including continuous and discrete monitoring systems generally include a small, lightweight battery powered and microprocessor controlled system which is configured to detect signals proportional to the corresponding measured glucose levels using an electrometer, and RF signals to transmit the collected data.
- One aspect of certain analyte monitoring systems include a transcutaneous or subcutaneous analyte sensor configuration which is, for example, partially mounted on the skin of a subject whose analyte level is to be monitored.
- the sensor cell may use a two or three-electrode (work, reference and counter electrodes) configuration driven by a controlled potential (potentiostat) analog circuit connected through a contact system.
- the analyte sensor may be configured so that a portion thereof is placed under the skin of the patient so as to detect the analyte levels of the patient, and another segment of the analyte sensor that is in communication with the transmitter unit.
- the transmitter unit is configured to transmit the analyte levels detected by the sensor over a wireless communication link such as an RF (radio frequency) communication link to a receiver/monitor unit.
- the receiver/monitor unit performs data analysis, among others on the received analyte levels to generate information pertaining to the monitored analyte levels.
- blood glucose measurements are obtained using, for example, a blood glucose meter, and the measured blood glucose values are used to calibrate the sensors. Due to a lag factor between the monitored sensor data and the measured blood glucose values, an error, or signal noise such as signal dropouts, is typically introduced in calibration using the monitored data as well as in computing the displayed glucose value. While correcting for the lag factors can minimize the error due to lag in the presence of noise, in the presence of signal dropouts, such error compensation may reduce accuracy of the monitored sensor data.
- a method for minimizing the effect of noise and signal dropouts in a glucose sensor including monitoring a data stream, generating a noise- filtered signal associated with the data stream, determining a presence of a signal dropout based on the noise filtered signal, and estimating a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout are disclosed.
- FIG. 1 illustrates a block diagram of a data monitoring and management system for practicing one or more embodiments of the present invention
- FIG. 2 is a block diagram of the transmitter unit of the data monitoring and management system shown in FIG. 1 in accordance with one embodiment of the present invention
- FIG. 3 is a block diagram of the receiver/monitor unit of the data monitoring and management system shown in FIG. 1 in accordance with one embodiment of the present invention
- FIG. 4 is a functional diagram of the overall signal processing for noise filtering and signal dropout compensation in accordance with one embodiment of the present invention
- FIG. 5 is a flowchart illustrating the overall signal processing for noise filtering and signal dropout compensation in accordance with one embodiment of the present invention
- FIG. 6 is a flowchart illustrating the process input estimation in accordance with one embodiment of the present invention.
- FIG. 7 is a flowchart illustrating the noise filtered estimation
- FIG. 8 is a flowchart illustrating signal dropout detection in accordance with one embodiment of the present invention.
- FIG. 9 is a flowchart illustrating an overall signal dropout compensation in accordance with one embodiment of the present invention.
- FIG. 10 is flowchart illustrating a detailed signal dropout compensation determination of FIG. 9 in accordance with one embodiment of the present invention.
- a method and system for providing noise filtered and/or signal dropout mitigated processes for signals in analyte monitoring systems there are provided method and system for noise filtering, signal dropout detection, and signal dropout compensation to improve the accuracy of lag compensation.
- FIG. 1 illustrates a data monitoring and management system such as, for example, analyte (e.g., glucose) monitoring system 100 in accordance with one embodiment of the present invention.
- analyte e.g., glucose
- the subject invention is further described primarily with respect to a glucose monitoring system for convenience and such description is in no way intended to limit the scope of the invention.
- the analyte monitoring system may be configured to monitor a variety of analytes, e.g., lactate, and the like.
- Analytes that may be monitored include, for example, acetyl choline, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glucose, glutamine, growth hormones, hormones, ketones, lactate, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin.
- concentration of drugs such as, for example, antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may also be monitored.
- the analyte monitoring system 100 includes a sensor 101, a transmitter unit 102 coupled to the sensor 101, and a primary receiver unit 104 which is configured to communicate with the transmitter unit 102 via a communication link 103.
- the primary receiver unit 104 may be further configured to transmit data to a data processing terminal 105 for evaluating the data received by the primary receiver unit 104.
- the data processing terminal in one embodiment may be configured to receive data directly from the transmitter unit 102 via a communication link 106 which may optionally be configured for bi-directional communication.
- a secondary receiver unit 106 which is operatively coupled to the communication link and configured to receive data transmitted from the transmitter unit 102. Moreover, as shown in the Figure, the secondary receiver unit 106 is configured to communicate with the primary receiver unit 104 as well as the data processing terminal 105. Indeed, the secondary receiver unit 106 may be configured for bi-directional wireless communication with each of the primary receiver unit 104 and the data processing terminal 105. As discussed in further detail below, in one embodiment of the present invention, the secondary receiver unit 106 may be configured to include a limited number of functions and features as compared with the primary receiver unit 104. As such, the secondary receiver unit 106 may be configured substantially in a smaller compact housing or embodied in a device such as a wrist watch, for example.
- the secondary receiver unit 106 may be configured with the same or substantially similar functionality as the primary receiver unit 104, and may be configured to be used in conjunction with a docking cradle unit for placement by bedside, for night time monitoring, and/or bi-directional communication device.
- the analyte monitoring system 100 may include one or more sensor 101, transmitter unit 102, communication link 103, and data processing terminal 105.
- the analyte monitoring system 100 may be a continuous monitoring system, or semi-continuous, or a discrete monitoring system. In a multi-component environment, each device is configured to be uniquely identified by each of the other devices in the system so that communication conflict is readily resolved between the various components within the analyte monitoring system 100.
- the senor 101 is physically positioned in or on the body of a user whose analyte level is being monitored.
- the sensor 101 may be configured to continuously sample the analyte level of the user and convert the sampled analyte level into a corresponding data signal for transmission by the transmitter unit 102.
- the transmitter unit 102 is mounted on the sensor 101 so that both devices are positioned on the user's body.
- the transmitter unit 102 performs data processing such as filtering and encoding on data signals, each of which corresponds to a sampled analyte level of the user, for transmission to the primary receiver unit 104 via the communication link 103.
- the analyte monitoring system 100 is configured as a oneway RF communication path from the transmitter unit 102 to the primary receiver unit 104.
- the transmitter unit 102 transmits the sampled data signals received from the sensor 101 without acknowledgement from the primary receiver unit 104 that the transmitted sampled data signals have been received.
- the transmitter unit 102 may be configured to transmit the encoded sampled data signals at a fixed rate (e.g., at one minute intervals) after the completion of the initial power on procedure.
- the primary receiver unit 104 may be configured to detect such transmitted encoded sampled data signals at predetermined time intervals.
- the analyte monitoring system 100 may be configured with a bidirectional RF (or otherwise) communication between the transmitter unit 102 and the primary receiver unit 104.
- the primary receiver unit 104 may include two sections.
- the first section is an analog interface section that is configured to communicate with the transmitter unit 102 via the communication link 103.
- the analog interface section may include an RF receiver and an antenna for receiving and amplifying the data signals from the transmitter unit 102, which are thereafter, demodulated with a local oscillator and filtered through a band-pass filter.
- the second section of the primary receiver unit 104 is a data processing section which is configured to process the data signals received from the transmitter unit 102 such as by performing data decoding, error detection and correction, data clock generation, and data bit recovery.
- the primary receiver unit 104 is configured to detect the presence of the transmitter unit 102 within its range based on, for example, the strength of the detected data signals received from the transmitter unit 102 or a predetermined transmitter identification information. Upon successful synchronization with the corresponding transmitter unit 102, the primary receiver unit 104 is configured to begin receiving from the transmitter unit 102 data signals corresponding to the user's detected analyte level. More specifically, the primary receiver unit 104 in one embodiment is configured to perform synchronized time hopping with the corresponding synchronized transmitter unit 102 via the communication link 103 to obtain the user's detected analyte level.
- the data processing terminal 105 may include a personal computer, a portable computer such as a laptop or a handheld device (e.g., personal digital assistants (PDAs)), and the like, each of which may be configured for data communication with the receiver via a wired or a wireless connection. Additionally, the data processing terminal 105 may further be connected to a data network (not shown) for storing, retrieving and updating data corresponding to the detected analyte level of the user.
- a data network not shown
- the data processing terminal 105 may include an infusion device such as an insulin infusion pump or the like, which may be configured to administer insulin to patients, and which may be configured to communicate with the receiver unit 104 for receiving, among others, the measured analyte level.
- the receiver unit 104 may be configured to integrate an infusion device therein so that the receiver unit 104 is configured to administer insulin therapy to patients, for example, for administering and modifying basal profiles, as well as for determining appropriate boluses for administration based on, among others, the detected analyte levels received from the transmitter unit 102.
- the transmitter unit 102, the primary receiver unit 104 and the data processing terminal 105 may each be configured for bi-directional wireless communication such that each of the transmitter unit 102, the primary receiver unit 104 and the data processing terminal 105 may be configured to communicate (that is, transmit data to and receive data from) with each other via the wireless communication link 103. More specifically, the data processing terminal 105 may in one embodiment be configured to receive data directly from the transmitter unit 102 via the communication link 106, where the communication link 106, as described above, may be configured for bi-directional communication.
- the data processing terminal 105 which may include an insulin pump, may be configured to receive the analyte signals from the transmitter unit 102, and thus, incorporate the functions of the receiver 103 including data processing for managing the patient's insulin therapy and analyte monitoring.
- the communication link 103 may include one or more of an RF communication protocol, an infrared communication protocol, a Bluetooth enabled communication protocol, an 802.1 Ix wireless communication protocol, or an equivalent wireless communication protocol which would allow secure, wireless communication of several units (for example, per HIPPA requirements) while avoiding potential data collision and interference.
- FIG. 2 is a block diagram of the transmitter of the data monitoring and detection system shown in FIG. 1 in accordance with one embodiment of the present invention.
- the transmitter unit 102 in one embodiment includes an analog interface 201 configured to communicate with the sensor 101 (FIG. 1), a user input 202, and a temperature detection section 203, each of which is operatively coupled to a transmitter processor 204 such as a central processing unit (CPU).
- a transmitter processor 204 such as a central processing unit (CPU).
- FIG. 2 there are provided four contacts, three of which are electrodes - work electrode (W) 210, guard contact (G) 211, reference electrode (R) 212, and counter electrode (C) 213, each operatively coupled to the analog interface 201 of the transmitter unit 102 for connection to the sensor unit 201 (FIG.
- each of the work electrode (W) 210, guard contact (G) 211, reference electrode (R) 212, and counter electrode (C) 213 may be made using a conductive material that is either printed or etched, for example, such as carbon which may be printed, or metal foil (e.g., gold) which may be etched.
- a transmitter serial communication section 205 and an RF transmitter 206 each of which is also operatively coupled to the transmitter processor 204.
- a power supply 207 such as a battery is also provided in the transmitter unit 102 to provide the necessary power for the transmitter unit 102.
- clock 208 is provided to, among others, supply real time information to the transmitter processor 204.
- a unidirectional input path is established from the sensor 101 (FIG. 1) and/or manufacturing and testing equipment to the analog interface 201 of the transmitter unit 102, while a unidirectional output is established from the output of the RF transmitter 206 of the transmitter unit 102 for transmission to the primary receiver unit 104.
- a data path is shown in FIG. 2 between the aforementioned unidirectional input and output via a dedicated link 209 from the analog interface 201 to serial communication section 205, thereafter to the processor 204, and then to the RF transmitter 206.
- the transmitter unit 102 is configured to transmit to the primary receiver unit 104 (FIG. 1), via the communication link 103 (FIG.
- the transmitter processor 204 is configured to transmit control signals to the various sections of the transmitter unit 102 during the operation of the transmitter unit 102.
- the transmitter processor 204 also includes a memory (not shown) for storing data such as the identification information for the transmitter unit 102, as well as the data signals received from the sensor 101. The stored information may be retrieved and processed for transmission to the primary receiver unit 104 under the control of the transmitter processor 204.
- the power supply 207 may include a commercially available battery.
- the transmitter unit 102 is also configured such that the power supply section 207 is capable of providing power to the transmitter for a minimum of about three months of continuous operation after having been stored for about eighteen months in a low-power (non-operating) mode. In one embodiment, this may be achieved by the transmitter processor 204 operating in low power modes in the non-operating state, for example, drawing no more than approximately 1 ⁇ A of current. Indeed, in one embodiment, the final step during the manufacturing process of the transmitter unit 102 may place the transmitter unit 102 in the lower power, non-operating state (i.e., post- manufacture sleep mode). In this manner, the shelf life of the transmitter unit 102 may be significantly improved. Moreover, as shown in FIG.
- the power supply unit 207 is shown as coupled to the processor 204, and as such, the processor 204 is configured to provide control of the power supply unit 207, it should be noted that within the scope of the present invention, the power supply unit 207 is configured to provide the necessary power to each of the components of the transmitter unit 102 shown in FIG. 2.
- the power supply section 207 of the transmitter unit 102 in one embodiment may include a rechargeable battery unit that may be recharged by a separate power supply recharging unit (for example, provided in the receiver unit 104) so that the transmitter unit 102 may be powered for a longer period of usage time.
- the transmitter unit 102 may be configured without a battery in the power supply section 207, in which case the transmitter unit 102 may be configured to receive power from an external power supply source (for example, a battery) as discussed in further detail below.
- an external power supply source for example, a battery
- the temperature detection section 203 of the transmitter unit 102 is configured to monitor the temperature of the skin near the sensor insertion site. The temperature reading is used to adjust the analyte readings obtained from the analog interface 201.
- the RF transmitter 206 of the transmitter unit 102 may be configured for operation in the frequency band of 315 MHz to 322 MHz, for example, in the United States. Further, in one embodiment, the RF transmitter 206 is configured to modulate the carrier frequency by performing Frequency Shift Keying and Manchester encoding. In one embodiment, the data transmission rate is 19,200 symbols per second, with a minimum transmission range for communication with the primary receiver unit 104.
- a leak detection circuit 214 coupled to the guard electrode (G) 211 and the processor 204 in the transmitter unit 102 of the data monitoring and management system 100.
- the leak detection circuit 214 in accordance with one embodiment of the present invention may be configured to detect leakage current in the sensor 101 to determine whether the measured sensor data are corrupt or whether the measured data from the sensor 101 is accurate.
- FIG. 3 is a block diagram of the receiver/monitor unit of the data monitoring and management system shown in FIG. 1 in accordance with one embodiment of the present invention.
- the primary receiver unit 104 includes a blood glucose test strip interface 301, an RF receiver 302, an input 303, a temperature detection section 304, and a clock 305, each of which is operatively coupled to a receiver processor 307.
- the primary receiver unit 104 also includes a power supply 306 operatively coupled to a power conversion and monitoring section 308. Further, the power conversion and monitoring section 308 is also coupled to the receiver processor 307.
- a receiver serial communication section 309, and an output 310 each operatively coupled to the receiver processor 307.
- the test strip interface 301 includes a glucose level testing portion to receive a manual insertion of a glucose test strip, and thereby determine and display the glucose level of the test strip on the output 310 of the primary receiver unit 104.
- This manual testing of glucose can be used to calibrate sensor 101.
- the RF receiver 302 is configured to communicate, via the communication link 103 (FIG. 1) with the RF transmitter 206 of the transmitter unit 102, to receive encoded data signals from the transmitter unit 102 for, among others, signal mixing, demodulation, and other data processing.
- the input 303 of the primary receiver unit 104 is configured to allow the user to enter information into the primary receiver unit 104 as needed.
- the input 303 may include one or more keys of a keypad, a touch-sensitive screen, or a voice-activated input command unit.
- the temperature detection section 304 is configured to provide temperature information of the primary receiver unit 104 to the receiver processor 307, while the clock 305 provides, among others, real time information to the receiver processor 307.
- Each of the various components of the primary receiver unit 104 shown in FIG. 3 is powered by the power supply 306 which, in one embodiment, includes a battery.
- the power conversion and monitoring section 308 is configured to monitor the power usage by the various components in the primary receiver unit 104 for effective power management and to alert the user, for example, in the event of power usage which renders the primary receiver unit 104 in sub-optimal operating conditions.
- An example of such sub-optimal operating condition may include, for example, operating the vibration output mode (as discussed below) for a period of time thus substantially draining the power supply 306 while the processor 307 (thus, the primary receiver unit 104) is turned on.
- the power conversion and monitoring section 308 may additionally be configured to include a reverse polarity protection circuit such as a field effect transistor (FET) configured as a battery activated switch.
- FET field effect transistor
- the serial communication section 309 in the primary receiver unit 104 is configured to provide a bi-directional communication path from the testing and/or manufacturing equipment for, among others, initialization, testing, and configuration of the primary receiver unit 104.
- Serial communication section 104 can also be used to upload data to a computer, such as time-stamped blood glucose data.
- the communication link with an external device can be made, for example, by cable, infrared (IR) or RF link.
- the output 310 of the primary receiver unit 104 is configured to provide, among others, a graphical user interface (GUI) such as a liquid crystal display (LCD) for displaying information.
- GUI graphical user interface
- the output 310 may also include an integrated speaker for outputting audible signals as well as to provide vibration output as commonly found in handheld electronic devices, such as mobile telephones presently available.
- the primary receiver unit 104 also includes an electro-luminescent lamp configured to provide backlighting to the output 310 for output visual display in dark ambient surroundings.
- the primary receiver unit 104 in one embodiment may also include a storage section such as a programmable, non- volatile memory device as part of the processor 307, or provided separately in the primary receiver unit 104, operative Iy coupled to the processor 307.
- the processor 307 is further configured to perform Manchester decoding as well as error detection and correction upon the encoded data signals received from the transmitter unit 102 via the communication link 103.
- FIG. 4 is a functional diagram of the overall signal processing for noise filtering and signal dropout compensation
- FIG. 5 shows a flowchart illustrating the overall signal processing for noise filtering and signal dropout compensation in accordance with one embodiment of the present invention.
- signals measured are received from, for example, the analyte sensor 101 (FIG. 1) and are provided to the state observer 410 which in one embodiment may be configured to provide prior or past noise filtered estimate to a process input estimator 420.
- the process input estimator 420 may be configured to generate a process input estimate based on the prior or past noise filtered estimate of the received or measured signal, which is then provided to the state observer 410.
- the process input estimate at a predetermined time t may be based on past noise filtered estimate of the signal.
- the state observer 410 may be configured to generate a noise filtered estimate of the measured or received signal is generated based on the current measured or received signal and the process input estimate received from the process input estimator 420.
- a noise filtered estimate of the signal at the latest time t may be determined.
- this routine of generating the process input estimate based on the past noise filtered estimate of the received or measured signal, and generating the noise filtered estimate of the signal based on the current received or measured signal and the current determined or generated process input estimate may be repeated for each measurement signal received, for example, from the analyte sensor 101 (FIG. 1). In this manner, in one aspect, the noise filtered signals corresponding to the measured or received sensor signals may be determined. Referring back to FIGS. 4 and 5, in one embodiment, with the noise filtered estimate, the presence of signal dropouts are detected based on, for example, the current and past noise filtered estimate of the received or measured signal.
- a dropout detector 430 may be configured to detect signal dropouts, and thereafter, detection of signal dropouts are provided to dropout compensator 440.
- the dropout detector 430 may be configured to generate a signal or notification associated with the detection of a signal dropout (as shown in FIG .4). That is, in one embodiment and as described in further detail below in conjunction with FIG. 8, the dropout detector 430 may be configured to detect or estimate the presence or absence of signal dropouts at the predetermined time.
- the dropout compensator 440 may be configured to generate an estimate of the noise filtered, dropout compensated signal when the signal dropout is detected (for example, by the dropout detector 430), by subtracting the estimate of the current dropout signal source from the present noise filtered estimate of the signal.
- the noise filtered signal dropout mitigated or compensated signal may be generated to improve accuracy of the measured or received signal from, for example, the analyte sensor 101 (FIG. 1).
- FIG. 6 is a flowchart illustrating the process input estimation in accordance with one embodiment of the present invention.
- a mean component of the process input estimate u m (t) based on past noise filtered estimate of the signal is generated.
- a series of five past noise-filtered estimate of the signal, Xi(t-5), Xi(t-4), x ⁇ t-3), Xi(t-2), X 1 (M) the mean component of the process input estimate at time t, u m (t) may be determined by taking the unweighted average of these signals as shown by the following relationship:
- the mean component of the process input estimate at time t may be determined by taking the weighted average of these signals as shown by the following relationship:
- the mean component of the process input estimate at time t based on recent past data may be determined using filtering techniques, such as, but not limited to FIR filters.
- the difference component of the process input estimate at any time t, Ud(t) may be generated by, for example, taking an averaged difference of a series of noise-filtered estimate of the signal from the recent past.
- an unweighted average of the last three past differences may be used in the following manner:
- the difference gain at any time t, Ka(t) is determined, for example, by using past noise-filtered estimate of the signal, X 1 , and/or the derived signals from X 1 .
- a band-limited rate Xi bandRate and a band-limited acceleration X 1 bandAcc may be determined at any time t, based solely on recent past values of X 1 .
- a functional relationship may be determined to ascertain the value of the difference gain IQ at any time t.
- a lookup table can be constructed that determines the value of the difference gain IQ given the values of X 1 bandRate and X 1 bandAcc as shown below:
- the difference gain IQ may be used to scale the contribution of the difference component of the process input estimate ua in the value of the process input estimate at a given time. For example, a relatively larger value of the difference gain IQ may indicate a larger contribution of the difference component of the process input estimate ua in the value of the process input estimate at the particular time, and so on.
- the lookup table may show the relationship between factors such as the band-limited rate X 1 bandRate and the band-limited acceleration Xi bandAcc upon how much the difference component of the process input estimate ua should contribute to the process input estimate value.
- the scaled difference component Ud s (t) of the process input estimate may be determined by multiplying the difference component of the process input estimate at any time t, U d (t) by the difference gain at any time t, IQ(t). Thereafter, the scaled difference component Ud s (t) of the process input estimate may be added to the mean component of the process input estimate u m (t) to determine the current process input estimate value u(t).
- FIG. 7 is a flowchart illustrating the noise filtered estimation.
- the state observer 410 may be configured to determine the estimate of noise-filtered signal at any time t, X 1 (T).
- the state observer 410 may be configured to reduce the contribution of noise without introducing excessive undesirable distortion based on the estimate of process input signal at any time t, u(t), and the measured signals from the sensor z(t).
- FIG. 8 is a flowchart illustrating signal dropout detection in accordance with one embodiment of the present invention.
- a present "fast rate" estimate Xdf(t) is determined based on present and past noise-filtered estimate of the signal.
- a difference signal X d (t) may be determined based on the following expression:
- a fast rate may be extracted from the difference signal xa(t) by performing high pass filtering on the difference signal Xd(t).
- a discrete-time realization of a first order high pass filter function may be used to determine the present "fast rate" estimate Xdf(t):
- X df (f) a hpfD x df (t - ⁇ ) + x d (t) - x d (t ⁇ 1) (6)
- a present "slow rate” estimate Xd s (t) is determined based on present and past noise-filtered estimate of the signal.
- the slow rate estimate X ds (t) may be determined by passing the simple difference through a low- pass filter, or alternatively, by taking the difference between the simple difference and the fast difference signals as shown, for example, by the following expression:
- the fast rate estimate Xds(t) After determining the slow rate estimate Xds(t), it is determined whether there is a beginning of a large negative spike in the fast rate estimate Xdf(t). That is, referring to FIG. 8, the start of a signal dropout state is determined which is correlated to a spike in the fast difference.
- the fast difference does not generate a spike larger than a predetermined value in response to signals generated in the absence of dropouts. For example, adjusted to the units of glucose concentration, this may correspond to a fast rate in excess of -3 mg/(dL min). Although a rate of -3 mg/(dL min) or faster may be ascertained, when band pass filtered, the fast rate estimate Xdf(t) determined above does not occur in this range unless a signal dropout occurs.
- a safety check mechanism may be provided to determine situations where a signal dropout that was anticipated to have started has lasted in an undesirably long period of dropout time period. That is, as the signal dropouts are generally intermittent in nature, it is expected that the dropout does not last beyond the order of one hour, for example, and more commonly, in the order of five to 30 minutes.
- a predetermined allowable time period As shown in FIG. 8, if it is determined the allowable time period has not elapsed, then the beginning or onset of the signal dropout is estimated. On the other hand, if the predetermined allowable time period has elapsed, then the end of the signal dropout is estimated. Referring again to FIG. 8, when the beginning of a large negative spike in the fast rate estimate Xdf(t) is not detected, it is determined whether an end of a large positive spike (for example, in the order of +3 mg/(dL min)) in the fast rate estimate X df (t) is detected.
- a signal dropout is generally correlated to a large positive spike in the fast difference.
- the tail of the large positive spike is monitored and detected as the end of the signal dropout. In one embodiment, this maximizes the likelihood of detecting most of the instances within a signal dropout.
- the presence of signal dropout may be monitored and detected based on, for example, present and past noise filtered estimate of the signals.
- FIG. 9 is a flowchart illustrating an overall signal dropout compensation in accordance with one embodiment of the present invention.
- a momentum-based estimate is determined based on the present slow difference and previous momentum-based estimate. That is, with the present and past noise filtered estimate of the signal, the present and past slow and fast rate estimates determined as described above, and with the signal dropout detection estimation determined above, the momentum-based estimate is determined based on the present slow difference and previous momentum-based estimate. That is, in one embodiment, a momentum-based estimate may factor in a signal without dropouts as being likely to project (e.g., extrapolate) based on its past signal and its prior trend.
- an averaged value of the present or current momentum-based estimate and the present noise filtered estimate is determined.
- an inertial gain based on the present and past slow rate estimate is determined, and which may be configured to scale the contribution of the momentum- based estimate determined using the present slow different and the previous momentum based estimate above in the final dropout compensated gain.
- a tracking gain is determined based on the inertial gain.
- the determined tracking gain may be configured to scale the impact of the determined average value of the present momentum-based estimate and the present noise-filtered estimate, in the determination of the final dropout compensated signal as discussed below.
- the dropout compensated signal is determined.
- the dropout-compensated signal equals the noise-filtered estimate of the signal X 1 , when no dropout is estimated.
- the dropout compensated signal may be a weighted average of the momentum-based estimate (x m omentum ) as discussed above and the averaged momentum and noise-filtered estimate (x aV era g e) also discussed above.
- the weighing factors for the weighted average of the momentum-based estimate (x m omentum ) and the averaged momentum and noise-filtered estimate (x aV erage) may be the inertial gain K iner tiai and tracking gain K trackmg , respectively.
- the dropout compensated signal at any time t, x'd ⁇ (t) in one embodiment may be determined in accordance with the following relationship:
- the determination of the dropout compensated signal at any time t, x'd ⁇ (t) may be refined to ensure a smooth transition depending upon the underlying conditions, as described in further detail below in conjunction with FIG. 10.
- the dropout compensated signal may be clipped to be within a predetermined range, for example, such that the dropout compensated signal is not less than the noise-filtered signal, and further, that it is not greater than a specified safety ratio times the noise- filtered signal.
- the resulting value of the dropout compensated signal x' d ⁇ (t) may fall below the noise-filtered estimate X 1 (I;). Since by definition, a dropout is a phenomena that can only reduce the true value of a signal, the relationship (8) above for determining the dropout compensated signal may be modified by ensuring that its value never goes below Xj(t) at any given time, and as shown by the following expression:
- FIG. 10 is flowchart illustrating a detailed signal dropout compensation determination of FIG. 9 in accordance with one embodiment of the present invention.
- the dropout compensated signal may be based upon the present noise filtered signal.
- the preset time period may be a predetermined time period that may be considered a long period of time.
- the dropout compensated signal may be based upon a smooth transition using the previous dropout compensated signal and the present noise filtered signal.
- the dropout compensated signal may be determined based on one or more factors as shown in the Figure and also described above.
- the processings associated with the noise filtering, signal dropout detection estimation and compensation may be performed by one or more processing units of the one or more receiver unit (104, 105) the transmitter unit 102 or the data processing terminal/infusion section 105.
- the one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105 may also incorporate a blood glucose meter functionality, such that, the housing of the respective one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105 may include a test strip port configured to receive a blood sample for determining one or more blood glucose levels of the patient.
- the one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105 may be configured to receive the blood glucose value wirelessly over a communication link from, for example, a glucose meter.
- the user or patient manipulating or using the analyte monitoring system 100 may manually input the blood glucose value using, for example, a user interface (for example, a keyboard, keypad, and the like) incorporated in the one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105.
- a method in one embodiment includes monitoring a data stream, generating a noise-filtered signal associated with the data stream, detecting a presence of a signal dropout based on the noise filtered signal, and estimating a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
- generating the noise filtered signal may include generating one or more frequency-shaped signals based on the monitored data stream, and further, which may include high pass filtering the monitored data stream.
- generating the noise filtered signal in another aspect may be based on one or more previous noise filtered signals.
- the method in a further embodiment may include outputting the noise filtered signal.
- the method in still another aspect may include outputting the noise filtered dropout compensated signal.
- the method may also include generating a signal associated with detecting the presence of a signal dropout.
- the data stream in one embodiment may be associated with a monitored analyte levels of a patient.
- An apparatus in another embodiment includes one or more processors, and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to monitor a data stream, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
- the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate one or more frequency-shaped signals based on the monitored data stream.
- the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate the one or more frequency shaped signals by high pass filtering the monitored data stream.
- the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate the noise filtered signal based on one or more previous noise filtered signals.
- the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to output the noise filtered signal.
- the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to output the noise filtered dropout compensated signal.
- the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate a signal associated with detecting the presence of a signal dropout.
- a system in accordance with still another embodiment may include an analyte sensor configured to monitor an analyte of a patient, a data processing section operatively coupled to the analyte sensor, the data processing section further including one or more processors, and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to monitor a data stream, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
- the data processing section may include a data transmission unit operatively coupled to one or more processors configured to transmit the data stream.
- the data processing section may include a data receiving unit operatively coupled to the one or more processors and configured to receive the data stream.
- the analyte sensor may include a glucose sensor.
- the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to store one or more of the data stream, the noise filtered signal, or the noise filtered dropout compensated signal.
- the various processes described above including the processes performed by the receiver unit 104/105 or transmitter unit 102 in the software application execution environment in the analyte monitoring system 100 including the processes and routines described in conjunction with FIGS. 5-10, may be embodied as computer programs developed using an object oriented language that allows the modeling of complex systems with modular objects to create abstractions that are representative of real world, physical objects and their interrelationships.
- the software required to carry out the inventive process, which may be stored in the memory or storage unit of the receiver unit 104/105 or transmitter unit 102 may be developed by a person of ordinary skill in the art and may include one or more computer program products.
Abstract
The effects of noise and signal dropouts are minimized in a glucose sensor by monitoring a data stream, generating a noise-filtered signal with the data stream, determining a presence of a signal dropout based on the noise filtered signal, and estimating a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
Description
METHOD AND SYSTEM FOR PROVIDING ANALYTE MONITORING
PRIORITY
This application claims priority to US patent application serial no. 11/552,935 filed October 25, 2006, entitled "Method And System For Providing Analyte Monitoring," the disclosure of which is incorporated herein by reference for all purposes.
BACKGROUND
Analyte, e.g., glucose monitoring systems including continuous and discrete monitoring systems generally include a small, lightweight battery powered and microprocessor controlled system which is configured to detect signals proportional to the corresponding measured glucose levels using an electrometer, and RF signals to transmit the collected data. One aspect of certain analyte monitoring systems include a transcutaneous or subcutaneous analyte sensor configuration which is, for example, partially mounted on the skin of a subject whose analyte level is to be monitored. The sensor cell may use a two or three-electrode (work, reference and counter electrodes) configuration driven by a controlled potential (potentiostat) analog circuit connected through a contact system.
The analyte sensor may be configured so that a portion thereof is placed under the skin of the patient so as to detect the analyte levels of the patient, and another segment of the analyte sensor that is in communication with the transmitter unit. The transmitter unit is configured to transmit the analyte levels detected by the sensor over a wireless communication link such as an RF (radio frequency) communication link to a receiver/monitor unit. The receiver/monitor unit performs data analysis, among others on the received analyte levels to generate information pertaining to the monitored analyte levels.
To obtain accurate data from the analyte sensor, calibration using capillary blood glucose measurements is necessary. Typically, blood glucose measurements are obtained using, for example, a blood glucose meter, and the measured blood glucose values are used to calibrate the sensors. Due to a lag factor between the monitored sensor data and the measured blood glucose values, an error, or signal noise such as
signal dropouts, is typically introduced in calibration using the monitored data as well as in computing the displayed glucose value. While correcting for the lag factors can minimize the error due to lag in the presence of noise, in the presence of signal dropouts, such error compensation may reduce accuracy of the monitored sensor data.
In view of the foregoing, it would be desirable to have a method and system for providing noise filtering and signal dropout detection and/or compensation in data monitoring systems.
SUMMARY OF THE INVENTION
In one embodiment, a method for minimizing the effect of noise and signal dropouts in a glucose sensor including monitoring a data stream, generating a noise- filtered signal associated with the data stream, determining a presence of a signal dropout based on the noise filtered signal, and estimating a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout are disclosed.
These and other objects, features and advantages of the present invention will become more fully apparent from the following detailed description of the embodiments, the appended claims and the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates a block diagram of a data monitoring and management system for practicing one or more embodiments of the present invention;
FIG. 2 is a block diagram of the transmitter unit of the data monitoring and management system shown in FIG. 1 in accordance with one embodiment of the present invention;
FIG. 3 is a block diagram of the receiver/monitor unit of the data monitoring and management system shown in FIG. 1 in accordance with one embodiment of the present invention;
FIG. 4 is a functional diagram of the overall signal processing for noise filtering and signal dropout compensation in accordance with one embodiment of the present invention;
FIG. 5 is a flowchart illustrating the overall signal processing for noise filtering and signal dropout compensation in accordance with one embodiment of the present invention;
FIG. 6 is a flowchart illustrating the process input estimation in accordance with one embodiment of the present invention;
FIG. 7 is a flowchart illustrating the noise filtered estimation;
FIG. 8 is a flowchart illustrating signal dropout detection in accordance with one embodiment of the present invention;
FIG. 9 is a flowchart illustrating an overall signal dropout compensation in accordance with one embodiment of the present invention; and
FIG. 10 is flowchart illustrating a detailed signal dropout compensation determination of FIG. 9 in accordance with one embodiment of the present invention.
DETAILED DESCRIPTION
As described in further detail below, in accordance with the various embodiments of the present invention, there is provided a method and system for providing noise filtered and/or signal dropout mitigated processes for signals in analyte monitoring systems. In particular, within the scope of the present invention, there are provided method and system for noise filtering, signal dropout detection, and signal dropout compensation to improve the accuracy of lag compensation.
FIG. 1 illustrates a data monitoring and management system such as, for example, analyte (e.g., glucose) monitoring system 100 in accordance with one embodiment of the present invention. The subject invention is further described primarily with respect to a glucose monitoring system for convenience and such description is in no way intended to limit the scope of the invention. It is to be understood that the analyte monitoring system may be configured to monitor a variety of analytes, e.g., lactate, and the like.
Analytes that may be monitored include, for example, acetyl choline, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glucose, glutamine, growth hormones, hormones, ketones, lactate, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin. The concentration of drugs, such as, for example, antibiotics (e.g.,
gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may also be monitored.
The analyte monitoring system 100 includes a sensor 101, a transmitter unit 102 coupled to the sensor 101, and a primary receiver unit 104 which is configured to communicate with the transmitter unit 102 via a communication link 103. The primary receiver unit 104 may be further configured to transmit data to a data processing terminal 105 for evaluating the data received by the primary receiver unit 104. Moreover, the data processing terminal in one embodiment may be configured to receive data directly from the transmitter unit 102 via a communication link 106 which may optionally be configured for bi-directional communication.
Also shown in FIG. 1 is a secondary receiver unit 106 which is operatively coupled to the communication link and configured to receive data transmitted from the transmitter unit 102. Moreover, as shown in the Figure, the secondary receiver unit 106 is configured to communicate with the primary receiver unit 104 as well as the data processing terminal 105. Indeed, the secondary receiver unit 106 may be configured for bi-directional wireless communication with each of the primary receiver unit 104 and the data processing terminal 105. As discussed in further detail below, in one embodiment of the present invention, the secondary receiver unit 106 may be configured to include a limited number of functions and features as compared with the primary receiver unit 104. As such, the secondary receiver unit 106 may be configured substantially in a smaller compact housing or embodied in a device such as a wrist watch, for example. Alternatively, the secondary receiver unit 106 may be configured with the same or substantially similar functionality as the primary receiver unit 104, and may be configured to be used in conjunction with a docking cradle unit for placement by bedside, for night time monitoring, and/or bi-directional communication device.
Only one sensor 101, transmitter unit 102, communication link 103, and data processing terminal 105 are shown in the embodiment of the analyte monitoring system 100 illustrated in FIG. 1. However, it will be appreciated by one of ordinary skill in the art that the analyte monitoring system 100 may include one or more sensor 101, transmitter unit 102, communication link 103, and data processing terminal 105. Moreover, within the scope of the present invention, the analyte monitoring system 100 may be a continuous monitoring system, or semi-continuous, or a discrete monitoring
system. In a multi-component environment, each device is configured to be uniquely identified by each of the other devices in the system so that communication conflict is readily resolved between the various components within the analyte monitoring system 100.
In one embodiment of the present invention, the sensor 101 is physically positioned in or on the body of a user whose analyte level is being monitored. The sensor 101 may be configured to continuously sample the analyte level of the user and convert the sampled analyte level into a corresponding data signal for transmission by the transmitter unit 102. In one embodiment, the transmitter unit 102 is mounted on the sensor 101 so that both devices are positioned on the user's body. The transmitter unit 102 performs data processing such as filtering and encoding on data signals, each of which corresponds to a sampled analyte level of the user, for transmission to the primary receiver unit 104 via the communication link 103.
In one embodiment, the analyte monitoring system 100 is configured as a oneway RF communication path from the transmitter unit 102 to the primary receiver unit 104. In such embodiment, the transmitter unit 102 transmits the sampled data signals received from the sensor 101 without acknowledgement from the primary receiver unit 104 that the transmitted sampled data signals have been received. For example, the transmitter unit 102 may be configured to transmit the encoded sampled data signals at a fixed rate (e.g., at one minute intervals) after the completion of the initial power on procedure. Likewise, the primary receiver unit 104 may be configured to detect such transmitted encoded sampled data signals at predetermined time intervals. Alternatively, the analyte monitoring system 100 may be configured with a bidirectional RF (or otherwise) communication between the transmitter unit 102 and the primary receiver unit 104.
Additionally, in one aspect, the primary receiver unit 104 may include two sections. The first section is an analog interface section that is configured to communicate with the transmitter unit 102 via the communication link 103. In one embodiment, the analog interface section may include an RF receiver and an antenna for receiving and amplifying the data signals from the transmitter unit 102, which are thereafter, demodulated with a local oscillator and filtered through a band-pass filter. The second section of the primary receiver unit 104 is a data processing section which is
configured to process the data signals received from the transmitter unit 102 such as by performing data decoding, error detection and correction, data clock generation, and data bit recovery.
In operation, upon completing the power-on procedure, the primary receiver unit 104 is configured to detect the presence of the transmitter unit 102 within its range based on, for example, the strength of the detected data signals received from the transmitter unit 102 or a predetermined transmitter identification information. Upon successful synchronization with the corresponding transmitter unit 102, the primary receiver unit 104 is configured to begin receiving from the transmitter unit 102 data signals corresponding to the user's detected analyte level. More specifically, the primary receiver unit 104 in one embodiment is configured to perform synchronized time hopping with the corresponding synchronized transmitter unit 102 via the communication link 103 to obtain the user's detected analyte level.
Referring again to FIG. 1, the data processing terminal 105 may include a personal computer, a portable computer such as a laptop or a handheld device (e.g., personal digital assistants (PDAs)), and the like, each of which may be configured for data communication with the receiver via a wired or a wireless connection. Additionally, the data processing terminal 105 may further be connected to a data network (not shown) for storing, retrieving and updating data corresponding to the detected analyte level of the user.
Within the scope of the present invention, the data processing terminal 105 may include an infusion device such as an insulin infusion pump or the like, which may be configured to administer insulin to patients, and which may be configured to communicate with the receiver unit 104 for receiving, among others, the measured analyte level. Alternatively, the receiver unit 104 may be configured to integrate an infusion device therein so that the receiver unit 104 is configured to administer insulin therapy to patients, for example, for administering and modifying basal profiles, as well as for determining appropriate boluses for administration based on, among others, the detected analyte levels received from the transmitter unit 102.
Additionally, the transmitter unit 102, the primary receiver unit 104 and the data processing terminal 105 may each be configured for bi-directional wireless communication such that each of the transmitter unit 102, the primary receiver unit 104
and the data processing terminal 105 may be configured to communicate (that is, transmit data to and receive data from) with each other via the wireless communication link 103. More specifically, the data processing terminal 105 may in one embodiment be configured to receive data directly from the transmitter unit 102 via the communication link 106, where the communication link 106, as described above, may be configured for bi-directional communication.
In this embodiment, the data processing terminal 105 which may include an insulin pump, may be configured to receive the analyte signals from the transmitter unit 102, and thus, incorporate the functions of the receiver 103 including data processing for managing the patient's insulin therapy and analyte monitoring. In one embodiment, the communication link 103 may include one or more of an RF communication protocol, an infrared communication protocol, a Bluetooth enabled communication protocol, an 802.1 Ix wireless communication protocol, or an equivalent wireless communication protocol which would allow secure, wireless communication of several units (for example, per HIPPA requirements) while avoiding potential data collision and interference.
FIG. 2 is a block diagram of the transmitter of the data monitoring and detection system shown in FIG. 1 in accordance with one embodiment of the present invention. Referring to the Figure, the transmitter unit 102 in one embodiment includes an analog interface 201 configured to communicate with the sensor 101 (FIG. 1), a user input 202, and a temperature detection section 203, each of which is operatively coupled to a transmitter processor 204 such as a central processing unit (CPU). As can be seen from FIG. 2, there are provided four contacts, three of which are electrodes - work electrode (W) 210, guard contact (G) 211, reference electrode (R) 212, and counter electrode (C) 213, each operatively coupled to the analog interface 201 of the transmitter unit 102 for connection to the sensor unit 201 (FIG. 1). In one embodiment, each of the work electrode (W) 210, guard contact (G) 211, reference electrode (R) 212, and counter electrode (C) 213 may be made using a conductive material that is either printed or etched, for example, such as carbon which may be printed, or metal foil (e.g., gold) which may be etched.
Further shown in FIG. 2 are a transmitter serial communication section 205 and an RF transmitter 206, each of which is also operatively coupled to the transmitter
processor 204. Moreover, a power supply 207 such as a battery is also provided in the transmitter unit 102 to provide the necessary power for the transmitter unit 102. Additionally, as can be seen from the Figure, clock 208 is provided to, among others, supply real time information to the transmitter processor 204.
In one embodiment, a unidirectional input path is established from the sensor 101 (FIG. 1) and/or manufacturing and testing equipment to the analog interface 201 of the transmitter unit 102, while a unidirectional output is established from the output of the RF transmitter 206 of the transmitter unit 102 for transmission to the primary receiver unit 104. In this manner, a data path is shown in FIG. 2 between the aforementioned unidirectional input and output via a dedicated link 209 from the analog interface 201 to serial communication section 205, thereafter to the processor 204, and then to the RF transmitter 206. As such, in one embodiment, via the data path described above, the transmitter unit 102 is configured to transmit to the primary receiver unit 104 (FIG. 1), via the communication link 103 (FIG. 1), processed and encoded data signals received from the sensor 101 (FIG. 1). Additionally, the unidirectional communication data path between the analog interface 201 and the RF transmitter 206 discussed above allows for the configuration of the transmitter unit 102 for operation upon completion of the manufacturing process as well as for direct communication for diagnostic and testing purposes.
As discussed above, the transmitter processor 204 is configured to transmit control signals to the various sections of the transmitter unit 102 during the operation of the transmitter unit 102. In one embodiment, the transmitter processor 204 also includes a memory (not shown) for storing data such as the identification information for the transmitter unit 102, as well as the data signals received from the sensor 101. The stored information may be retrieved and processed for transmission to the primary receiver unit 104 under the control of the transmitter processor 204. Furthermore, the power supply 207 may include a commercially available battery.
The transmitter unit 102 is also configured such that the power supply section 207 is capable of providing power to the transmitter for a minimum of about three months of continuous operation after having been stored for about eighteen months in a low-power (non-operating) mode. In one embodiment, this may be achieved by the transmitter processor 204 operating in low power modes in the non-operating state, for
example, drawing no more than approximately 1 μA of current. Indeed, in one embodiment, the final step during the manufacturing process of the transmitter unit 102 may place the transmitter unit 102 in the lower power, non-operating state (i.e., post- manufacture sleep mode). In this manner, the shelf life of the transmitter unit 102 may be significantly improved. Moreover, as shown in FIG. 2, while the power supply unit 207 is shown as coupled to the processor 204, and as such, the processor 204 is configured to provide control of the power supply unit 207, it should be noted that within the scope of the present invention, the power supply unit 207 is configured to provide the necessary power to each of the components of the transmitter unit 102 shown in FIG. 2.
Referring back to FIG. 2, the power supply section 207 of the transmitter unit 102 in one embodiment may include a rechargeable battery unit that may be recharged by a separate power supply recharging unit (for example, provided in the receiver unit 104) so that the transmitter unit 102 may be powered for a longer period of usage time. Moreover, in one embodiment, the transmitter unit 102 may be configured without a battery in the power supply section 207, in which case the transmitter unit 102 may be configured to receive power from an external power supply source (for example, a battery) as discussed in further detail below.
Referring yet again to FIG. 2, the temperature detection section 203 of the transmitter unit 102 is configured to monitor the temperature of the skin near the sensor insertion site. The temperature reading is used to adjust the analyte readings obtained from the analog interface 201. The RF transmitter 206 of the transmitter unit 102 may be configured for operation in the frequency band of 315 MHz to 322 MHz, for example, in the United States. Further, in one embodiment, the RF transmitter 206 is configured to modulate the carrier frequency by performing Frequency Shift Keying and Manchester encoding. In one embodiment, the data transmission rate is 19,200 symbols per second, with a minimum transmission range for communication with the primary receiver unit 104.
Referring yet again to FIG. 2, also shown is a leak detection circuit 214 coupled to the guard electrode (G) 211 and the processor 204 in the transmitter unit 102 of the data monitoring and management system 100. The leak detection circuit 214 in accordance with one embodiment of the present invention may be configured to detect
leakage current in the sensor 101 to determine whether the measured sensor data are corrupt or whether the measured data from the sensor 101 is accurate.
Additional detailed description of the continuous analyte monitoring system, its various components including the functional descriptions of the transmitter are provided in U.S. Patent No. 6,175,752 issued January 16, 2001 entitled "Analyte Monitoring Device and Methods of Use", and in application No. 10/745,878 filed December 26, 2003 entitled "Continuous Glucose Monitoring System and Methods of Use", each assigned to the Assignee of the present application.
FIG. 3 is a block diagram of the receiver/monitor unit of the data monitoring and management system shown in FIG. 1 in accordance with one embodiment of the present invention. Referring to FIG. 3, the primary receiver unit 104 includes a blood glucose test strip interface 301, an RF receiver 302, an input 303, a temperature detection section 304, and a clock 305, each of which is operatively coupled to a receiver processor 307. As can be further seen from the Figure, the primary receiver unit 104 also includes a power supply 306 operatively coupled to a power conversion and monitoring section 308. Further, the power conversion and monitoring section 308 is also coupled to the receiver processor 307. Moreover, also shown are a receiver serial communication section 309, and an output 310, each operatively coupled to the receiver processor 307.
In one embodiment, the test strip interface 301 includes a glucose level testing portion to receive a manual insertion of a glucose test strip, and thereby determine and display the glucose level of the test strip on the output 310 of the primary receiver unit 104. This manual testing of glucose can be used to calibrate sensor 101. The RF receiver 302 is configured to communicate, via the communication link 103 (FIG. 1) with the RF transmitter 206 of the transmitter unit 102, to receive encoded data signals from the transmitter unit 102 for, among others, signal mixing, demodulation, and other data processing. The input 303 of the primary receiver unit 104 is configured to allow the user to enter information into the primary receiver unit 104 as needed. In one aspect, the input 303 may include one or more keys of a keypad, a touch-sensitive screen, or a voice-activated input command unit. The temperature detection section 304 is configured to provide temperature information of the primary receiver unit 104 to the
receiver processor 307, while the clock 305 provides, among others, real time information to the receiver processor 307.
Each of the various components of the primary receiver unit 104 shown in FIG. 3 is powered by the power supply 306 which, in one embodiment, includes a battery. Furthermore, the power conversion and monitoring section 308 is configured to monitor the power usage by the various components in the primary receiver unit 104 for effective power management and to alert the user, for example, in the event of power usage which renders the primary receiver unit 104 in sub-optimal operating conditions. An example of such sub-optimal operating condition may include, for example, operating the vibration output mode (as discussed below) for a period of time thus substantially draining the power supply 306 while the processor 307 (thus, the primary receiver unit 104) is turned on. Moreover, the power conversion and monitoring section 308 may additionally be configured to include a reverse polarity protection circuit such as a field effect transistor (FET) configured as a battery activated switch.
The serial communication section 309 in the primary receiver unit 104 is configured to provide a bi-directional communication path from the testing and/or manufacturing equipment for, among others, initialization, testing, and configuration of the primary receiver unit 104. Serial communication section 104 can also be used to upload data to a computer, such as time-stamped blood glucose data. The communication link with an external device (not shown) can be made, for example, by cable, infrared (IR) or RF link. The output 310 of the primary receiver unit 104 is configured to provide, among others, a graphical user interface (GUI) such as a liquid crystal display (LCD) for displaying information. Additionally, the output 310 may also include an integrated speaker for outputting audible signals as well as to provide vibration output as commonly found in handheld electronic devices, such as mobile telephones presently available. In a further embodiment, the primary receiver unit 104 also includes an electro-luminescent lamp configured to provide backlighting to the output 310 for output visual display in dark ambient surroundings.
Referring back to FIG. 3, the primary receiver unit 104 in one embodiment may also include a storage section such as a programmable, non- volatile memory device as part of the processor 307, or provided separately in the primary receiver unit 104, operative Iy coupled to the processor 307. The processor 307 is further configured to
perform Manchester decoding as well as error detection and correction upon the encoded data signals received from the transmitter unit 102 via the communication link 103.
FIG. 4 is a functional diagram of the overall signal processing for noise filtering and signal dropout compensation, while FIG. 5 shows a flowchart illustrating the overall signal processing for noise filtering and signal dropout compensation in accordance with one embodiment of the present invention. Referring to the Figures, in one embodiment, signals measured are received from, for example, the analyte sensor 101 (FIG. 1) and are provided to the state observer 410 which in one embodiment may be configured to provide prior or past noise filtered estimate to a process input estimator 420.
In one embodiment, the process input estimator 420 may be configured to generate a process input estimate based on the prior or past noise filtered estimate of the received or measured signal, which is then provided to the state observer 410. In one aspect, and as described in further detail below in conjunction with FIG. 6, the process input estimate at a predetermined time t may be based on past noise filtered estimate of the signal.
Thereafter, in one embodiment, the state observer 410 may be configured to generate a noise filtered estimate of the measured or received signal is generated based on the current measured or received signal and the process input estimate received from the process input estimator 420. In one embodiment and as described in further detail below in conjunction with FIG. 7, using the real time process input and sensor measurement signals, a noise filtered estimate of the signal at the latest time t may be determined.
In one aspect, this routine of generating the process input estimate based on the past noise filtered estimate of the received or measured signal, and generating the noise filtered estimate of the signal based on the current received or measured signal and the current determined or generated process input estimate may be repeated for each measurement signal received, for example, from the analyte sensor 101 (FIG. 1). In this manner, in one aspect, the noise filtered signals corresponding to the measured or received sensor signals may be determined.
Referring back to FIGS. 4 and 5, in one embodiment, with the noise filtered estimate, the presence of signal dropouts are detected based on, for example, the current and past noise filtered estimate of the received or measured signal. More specifically, in one embodiment, a dropout detector 430 may be configured to detect signal dropouts, and thereafter, detection of signal dropouts are provided to dropout compensator 440. In one aspect, the dropout detector 430 may be configured to generate a signal or notification associated with the detection of a signal dropout (as shown in FIG .4). That is, in one embodiment and as described in further detail below in conjunction with FIG. 8, the dropout detector 430 may be configured to detect or estimate the presence or absence of signal dropouts at the predetermined time.
In one embodiment, the dropout compensator 440 may be configured to generate an estimate of the noise filtered, dropout compensated signal when the signal dropout is detected (for example, by the dropout detector 430), by subtracting the estimate of the current dropout signal source from the present noise filtered estimate of the signal. In this manner, and as described in further detail below in conjunction with FIGS. 9-10, in one embodiment of the present invention, the noise filtered signal dropout mitigated or compensated signal may be generated to improve accuracy of the measured or received signal from, for example, the analyte sensor 101 (FIG. 1).
FIG. 6 is a flowchart illustrating the process input estimation in accordance with one embodiment of the present invention. Referring to FIG. 6, a mean component of the process input estimate um(t) based on past noise filtered estimate of the signal is generated. For example, in one embodiment, a series of five past noise-filtered estimate of the signal, Xi(t-5), Xi(t-4), x^t-3), Xi(t-2), X1(M), the mean component of the process input estimate at time t, um(t) may be determined by taking the unweighted average of these signals as shown by the following relationship:
xl {t - 5) + xl {t - 4) + xl {t - S' ) + xl {t - 2) + xl {t - \) um {t) = (1)
Alternatively, the mean component of the process input estimate at time t may be determined by taking the weighted average of these signals as shown by the following relationship:
where the determination of the constants als a2, a3, a4, as, may be obtained based on empirical or analytical analysis of the analyte monitoring system.
In yet another embodiment, the mean component of the process input estimate at time t based on recent past data may be determined using filtering techniques, such as, but not limited to FIR filters.
Referring to FIG. 6, with the mean component of the process input estimate um(t) based on past noise filtered estimate of the signal determined, the difference component of the process input estimate at any time t, Ud(t), may be generated by, for example, taking an averaged difference of a series of noise-filtered estimate of the signal from the recent past. In one aspect, an unweighted average of the last three past differences may be used in the following manner:
uΛt) _ (x1 (t - 4) - x1 (t - 3)) + (x1 (t - 3) - x1 (t - 2)) + (x1 (t - 2) - x1 (t - l)) (3)
Within the scope of the present invention, other approaches such as the use of FIR filter to determine the proper number of recent past values of X1 as well as the weighting of each difference may be used.
Referring again to FIG. 6, after determining the difference component of the process input estimate at any time t, ua(t), the difference gain at any time t, Ka(t), is determined, for example, by using past noise-filtered estimate of the signal, X1, and/or the derived signals from X1. For example, in one embodiment, a band-limited rate Xi bandRate and a band-limited acceleration X1 bandAcc may be determined at any time t, based solely on recent past values of X1. Using the knowledge of how the amount of ua would contribute to the total process input u at any time t relates to these two variables Xi bandRate and X1 bandAcc, a functional relationship may be determined to ascertain the value of the difference gain IQ at any time t.
Alternatively, a lookup table can be constructed that determines the value of the difference gain IQ given the values of X1 bandRate and X1 bandAcc as shown below:
In one aspect, the difference gain IQ may be used to scale the contribution of the difference component of the process input estimate ua in the value of the process input estimate at a given time. For example, a relatively larger value of the difference gain IQ may indicate a larger contribution of the difference component of the process input estimate ua in the value of the process input estimate at the particular time, and so on. In this manner, in one aspect, the lookup table may show the relationship between factors such as the band-limited rate X1 bandRate and the band-limited acceleration Xi bandAcc upon how much the difference component of the process input estimate ua should contribute to the process input estimate value.
Referring again to FIG. 6, with the mean component of the process input estimate um(t), the difference component of the process input estimate at any time t, Ud(t), and the difference gain at any time t, IQ(t), the scaled difference component Uds(t) of the process input estimate may be determined by multiplying the difference component of the process input estimate at any time t, Ud(t) by the difference gain at any time t, IQ(t). Thereafter, the scaled difference component Uds(t) of the process input estimate may be added to the mean component of the process input estimate um(t) to determine the current process input estimate value u(t).
FIG. 7 is a flowchart illustrating the noise filtered estimation. Referring to FIG. 7, with an estimate of process input signal at any time t, u(t), and based on the measured signals from the analyte sensor z(t), in addition to past estimates of the noise-filtered signal X1(Vl), Xi(t-2), ..., the state observer 410 (FIG. 4) may be configured to determine the estimate of noise-filtered signal at any time t, X1(T). In one aspect, the state observer 410 (FIG. 4) may be configured to reduce the contribution of noise without introducing
excessive undesirable distortion based on the estimate of process input signal at any time t, u(t), and the measured signals from the sensor z(t).
FIG. 8 is a flowchart illustrating signal dropout detection in accordance with one embodiment of the present invention. Referring to FIG. 8, a present "fast rate" estimate Xdf(t) is determined based on present and past noise-filtered estimate of the signal. For example, a difference signal Xd(t) may be determined based on the following expression:
Thereafter, a fast rate may be extracted from the difference signal xa(t) by performing high pass filtering on the difference signal Xd(t). In one embodiment, a discrete-time realization of a first order high pass filter function may be used to determine the present "fast rate" estimate Xdf(t):
Xdf (f) = ahpfD xdf (t - ϊ) + xd (t) - xd (t ~ 1) (6)
where the value of ahpfo, or the structure of the high pass filter may be determined in accordance with the suitable design configurations, for example, a value between zero and one. Referring back to FIG. 8, after determining the "fast rate" estimate Xdf(t), a present "slow rate" estimate Xds(t) is determined based on present and past noise-filtered estimate of the signal. For example, in one embodiment, the slow rate estimate Xds(t) may be determined by passing the simple difference through a low- pass filter, or alternatively, by taking the difference between the simple difference and the fast difference signals as shown, for example, by the following expression:
xc!s (t) = xd (t) - xdf (t) (7)
After determining the slow rate estimate Xds(t), it is determined whether there is a beginning of a large negative spike in the fast rate estimate Xdf(t). That is, referring to FIG. 8, the start of a signal dropout state is determined which is correlated to a spike in the fast difference. The fast difference does not generate a spike larger than a
predetermined value in response to signals generated in the absence of dropouts. For example, adjusted to the units of glucose concentration, this may correspond to a fast rate in excess of -3 mg/(dL min). Although a rate of -3 mg/(dL min) or faster may be ascertained, when band pass filtered, the fast rate estimate Xdf(t) determined above does not occur in this range unless a signal dropout occurs.
Referring back to FIG. 8, if the beginning of a large negative spike in the fast rate estimate Xdf(t) is detected, then the elapsed time period from the initial occurrence of the large negative spike is monitored, for example, by triggering a timer or a counter so as to monitor the elapsed time since the most recent signal dropout occurrence predicted estimate. In this manner, a safety check mechanism may be provided to determine situations where a signal dropout that was anticipated to have started has lasted in an undesirably long period of dropout time period. That is, as the signal dropouts are generally intermittent in nature, it is expected that the dropout does not last beyond the order of one hour, for example, and more commonly, in the order of five to 30 minutes.
Thereafter, it is determined whether a predetermined allowable time period has elapsed. As shown in FIG. 8, if it is determined the allowable time period has not elapsed, then the beginning or onset of the signal dropout is estimated. On the other hand, if the predetermined allowable time period has elapsed, then the end of the signal dropout is estimated. Referring again to FIG. 8, when the beginning of a large negative spike in the fast rate estimate Xdf(t) is not detected, it is determined whether an end of a large positive spike (for example, in the order of +3 mg/(dL min)) in the fast rate estimate Xdf(t) is detected. If the end of the large positive spike in the fast rate estimate Xdf(t) is detected, then the end of the signal dropout is estimated. On the other hand, if the end of the large positive spike in the fast rate estimate Xdf(t) is not detected, then no signal dropout is estimated.
That is, a signal dropout is generally correlated to a large positive spike in the fast difference. Thus, in this case, the tail of the large positive spike is monitored and detected as the end of the signal dropout. In one embodiment, this maximizes the likelihood of detecting most of the instances within a signal dropout.
In this manner, in one embodiment of the present invention, the presence of signal dropout may be monitored and detected based on, for example, present and past noise filtered estimate of the signals.
FIG. 9 is a flowchart illustrating an overall signal dropout compensation in accordance with one embodiment of the present invention. Referring to FIG. 9, a momentum-based estimate is determined based on the present slow difference and previous momentum-based estimate. That is, with the present and past noise filtered estimate of the signal, the present and past slow and fast rate estimates determined as described above, and with the signal dropout detection estimation determined above, the momentum-based estimate is determined based on the present slow difference and previous momentum-based estimate. That is, in one embodiment, a momentum-based estimate may factor in a signal without dropouts as being likely to project (e.g., extrapolate) based on its past signal and its prior trend.
Referring back to FIG. 9, after determining the momentum based estimate using the present slow difference and prior momentum-based estimate, an averaged value of the present or current momentum-based estimate and the present noise filtered estimate is determined. Thereafter, an inertial gain based on the present and past slow rate estimate is determined, and which may be configured to scale the contribution of the momentum- based estimate determined using the present slow different and the previous momentum based estimate above in the final dropout compensated gain. Referring again to FIG. 9, after determining the inertial gain, a tracking gain is determined based on the inertial gain. In one embodiment, the determined tracking gain may be configured to scale the impact of the determined average value of the present momentum-based estimate and the present noise-filtered estimate, in the determination of the final dropout compensated signal as discussed below.
Referring to FIG. 9, after determining the tracking gain, the dropout compensated signal is determined. In one embodiment, the dropout-compensated signal equals the noise-filtered estimate of the signal X1, when no dropout is estimated. Otherwise, the dropout compensated signal may be a weighted average of the momentum-based estimate (xmomentum ) as discussed above and the averaged momentum and noise-filtered estimate (xaVerage) also discussed above. In one aspect, the weighing factors for the weighted average of the momentum-based estimate (xmomentum ) and the
averaged momentum and noise-filtered estimate (xaVerage) may be the inertial gain Kinertiai and tracking gain Ktrackmg, respectively. For example, the dropout compensated signal at any time t, x'dα(t) in one embodiment may be determined in accordance with the following relationship:
X'dc, (0 = \K,nert,al (0 X momentum (O) + \K trachng (0 X average (Oj (8)
In a further embodiment, the determination of the dropout compensated signal at any time t, x'dα(t) may be refined to ensure a smooth transition depending upon the underlying conditions, as described in further detail below in conjunction with FIG. 10.
Referring back to FIG. 9, after determining the dropout compensated signal, the dropout compensated signal may be clipped to be within a predetermined range, for example, such that the dropout compensated signal is not less than the noise-filtered signal, and further, that it is not greater than a specified safety ratio times the noise- filtered signal.
In certain cases, the resulting value of the dropout compensated signal x'dα(t) may fall below the noise-filtered estimate X1(I;). Since by definition, a dropout is a phenomena that can only reduce the true value of a signal, the relationship (8) above for determining the dropout compensated signal may be modified by ensuring that its value never goes below Xj(t) at any given time, and as shown by the following expression:
FIG. 10 is flowchart illustrating a detailed signal dropout compensation determination of FIG. 9 in accordance with one embodiment of the present invention. Referring to FIG. 10, for example, in determining the drop-compensated signal, it is first determined whether signal dropout is detected. If signal dropout is not detected, then it is determined whether a preset time period has elapsed since the end of the last dropout occurrence. If it is determined that a preset time period has elapsed, then the dropout compensated signal may be based upon the present noise filtered signal. In one aspect, the preset time period may be a predetermined time period that may be
considered a long period of time. On the other hand, if it is determined that the preset time period has not elapsed (that is, the end of the occurrence of a signal dropout has recently occurred), then the dropout compensated signal may be based upon a smooth transition using the previous dropout compensated signal and the present noise filtered signal.
Indeed, referring to FIG. 10, it can be seen that depending upon the determination of the timing of the signal dropout occurrence, in particular embodiments, the dropout compensated signal may be determined based on one or more factors as shown in the Figure and also described above.
Referring again to the Figures, in particular embodiments, the processings associated with the noise filtering, signal dropout detection estimation and compensation may be performed by one or more processing units of the one or more receiver unit (104, 105) the transmitter unit 102 or the data processing terminal/infusion section 105. In addition, the one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105 may also incorporate a blood glucose meter functionality, such that, the housing of the respective one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105 may include a test strip port configured to receive a blood sample for determining one or more blood glucose levels of the patient.
In a further embodiment, the one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105 may be configured to receive the blood glucose value wirelessly over a communication link from, for example, a glucose meter. In still a further embodiment, the user or patient manipulating or using the analyte monitoring system 100 (FIG. 1) may manually input the blood glucose value using, for example, a user interface (for example, a keyboard, keypad, and the like) incorporated in the one or more of the transmitter unit 102, the primary receiver unit 104, secondary receiver unit 105, or the data processing terminal/infusion section 105.
A method in one embodiment includes monitoring a data stream, generating a noise-filtered signal associated with the data stream, detecting a presence of a signal dropout based on the noise filtered signal, and estimating a noise filtered dropout
compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
In one aspect, generating the noise filtered signal may include generating one or more frequency-shaped signals based on the monitored data stream, and further, which may include high pass filtering the monitored data stream.
Also, generating the noise filtered signal in another aspect may be based on one or more previous noise filtered signals.
The method in a further embodiment may include outputting the noise filtered signal. The method in still another aspect may include outputting the noise filtered dropout compensated signal.
The method may also include generating a signal associated with detecting the presence of a signal dropout.
Moreover, the data stream in one embodiment may be associated with a monitored analyte levels of a patient.
An apparatus in another embodiment includes one or more processors, and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to monitor a data stream, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
The memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate one or more frequency-shaped signals based on the monitored data stream.
In another aspect, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate the one or more frequency shaped signals by high pass filtering the monitored data stream.
In still another aspect, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate the noise filtered signal based on one or more previous noise filtered signals.
Moreover, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to output the noise filtered signal.
In yet another embodiment, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to output the noise filtered dropout compensated signal.
Additionally, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate a signal associated with detecting the presence of a signal dropout.
A system in accordance with still another embodiment may include an analyte sensor configured to monitor an analyte of a patient, a data processing section operatively coupled to the analyte sensor, the data processing section further including one or more processors, and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to monitor a data stream, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
The data processing section may include a data transmission unit operatively coupled to one or more processors configured to transmit the data stream. In another aspect, the data processing section may include a data receiving unit operatively coupled to the one or more processors and configured to receive the data stream.
The analyte sensor may include a glucose sensor.
Moreover, the memory may be further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to store one or more of the data stream, the noise filtered signal, or the noise filtered dropout compensated signal.
The various processes described above including the processes performed by the receiver unit 104/105 or transmitter unit 102 in the software application execution environment in the analyte monitoring system 100 including the processes and routines described in conjunction with FIGS. 5-10, may be embodied as computer programs
developed using an object oriented language that allows the modeling of complex systems with modular objects to create abstractions that are representative of real world, physical objects and their interrelationships. The software required to carry out the inventive process, which may be stored in the memory or storage unit of the receiver unit 104/105 or transmitter unit 102 may be developed by a person of ordinary skill in the art and may include one or more computer program products.
Various other modifications and alterations in the structure and method of operation of this invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. It is intended that the following claims define the scope of the present invention and that structures and methods within the scope of these claims and their equivalents be covered thereby.
Claims
1. A method, comprising: monitoring a data stream; generating a noise-filtered signal associated with the data stream; detecting a presence of a signal dropout based on the noise filtered signal; and estimating a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
2. The method of claim 1 wherein generating the noise filtered signal includes generating one or more frequency-shaped signals based on the monitored data stream.
3. The method of claim 2 wherein generating the one or more frequency shaped signals include high pass filtering the monitored data stream.
4. The method of claim 1 wherein generating the noise filtered signal is based on one or more previous noise filtered signals.
5. The method of claim 1 further including outputting the noise filtered signal.
6. The method of claim 1 further including outputting the noise filtered dropout compensated signal.
7. The method of claim 1 further including a generating signal associated with detecting the presence of a signal dropout.
8. The method of claim 1 wherein the data stream is associated with a monitored analyte levels of a patient.
9. An apparatus, comprising: one or more processors; and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to monitor a data stream, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
10. The apparatus of claim 9 wherein the memory is further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate one or more frequency-shaped signals based on the monitored data stream.
11. The apparatus of claim 10 wherein the memory is further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate the one or more frequency shaped signals by high pass filtering the monitored data stream.
12. The apparatus of claim 9 wherein the memory is further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate the noise filtered signal based on one or more previous noise filtered signals.
13. The apparatus of claim 9 wherein the memory is further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to output the noise filtered signal.
14. The apparatus of claim 9 wherein the memory is further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to output the noise filtered dropout compensated signal.
15. The apparatus of claim 9 wherein the memory is further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to generate a signal associated with detecting the presence of a signal dropout.
16. A system, comprising: an analyte sensor configured to monitor an analyte of a patient; and a data processing section operatively coupled to the analyte sensor, the data processing section further including: one or more processors; and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to monitor a data stream, generate a noise-filtered signal associated with the data stream, detect a presence of a signal dropout based on the noise filtered signal, and estimate a noise filtered dropout compensated signal based on the noise filtered signal and the determination of the presence of the signal dropout.
17. The system of claim 16 wherein the data processing section includes a data transmission unit operatively coupled to one or more processors configured to transmit the data stream.
18. The system of claim 16 wherein the data processing section includes a data receiving unit operatively coupled to the one or more processors and configured to receive the data stream.
19. The system of claim 16 wherein the analyte sensor includes a glucose sensor.
20. The system of claim 16 wherein the memory is further configured for storing instructions which, when executed by the one or more processors, causes the one or more processors to store one or more of the data stream, the noise filtered signal, or the noise filtered dropout compensated signal.
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CA2667930A CA2667930C (en) | 2006-10-25 | 2007-10-24 | Method and system for providing analyte monitoring |
EP07854382A EP2114241A4 (en) | 2006-10-25 | 2007-10-24 | Method and system for providing analyte monitoring |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/552,935 | 2006-10-25 | ||
US11/552,935 US7630748B2 (en) | 2006-10-25 | 2006-10-25 | Method and system for providing analyte monitoring |
Publications (2)
Publication Number | Publication Date |
---|---|
WO2008052057A2 true WO2008052057A2 (en) | 2008-05-02 |
WO2008052057A3 WO2008052057A3 (en) | 2008-08-28 |
Family
ID=39325387
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2007/082382 WO2008052057A2 (en) | 2006-10-25 | 2007-10-24 | Method and system for providing analyte monitoring |
Country Status (4)
Country | Link |
---|---|
US (8) | US7630748B2 (en) |
EP (1) | EP2114241A4 (en) |
CA (1) | CA2667930C (en) |
WO (1) | WO2008052057A2 (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8579473B2 (en) | 2008-09-12 | 2013-11-12 | Koninklijke Philips N.V. | Luminaire for indirect illumination |
US9020572B2 (en) | 2008-02-21 | 2015-04-28 | Dexcom, Inc. | Systems and methods for processing, transmitting and displaying sensor data |
US9041730B2 (en) | 2010-02-12 | 2015-05-26 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
EP2890297A4 (en) * | 2012-08-30 | 2016-04-13 | Abbott Diabetes Care Inc | Dropout detection in continuous analyte monitoring data during data excursions |
US9662056B2 (en) | 2008-09-30 | 2017-05-30 | Abbott Diabetes Care Inc. | Optimizing analyte sensor calibration |
US9882660B2 (en) | 2006-10-26 | 2018-01-30 | Abbott Diabetes Care Inc. | Method, system and computer program product for real-time detection of sensitivity decline in analyte sensors |
Families Citing this family (92)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20190357827A1 (en) | 2003-08-01 | 2019-11-28 | Dexcom, Inc. | Analyte sensor |
EP2394572A3 (en) | 2004-12-23 | 2012-05-09 | ResMed Ltd. | Apparatus for detecting and discriminating breathing patterns from respiratory signals |
US10512429B2 (en) * | 2004-12-23 | 2019-12-24 | ResMed Pty Ltd | Discrimination of cheyne-stokes breathing patterns by use of oximetry signals |
US9636450B2 (en) | 2007-02-19 | 2017-05-02 | Udo Hoss | Pump system modular components for delivering medication and analyte sensing at seperate insertion sites |
CN102440785A (en) | 2005-08-31 | 2012-05-09 | 弗吉尼亚大学专利基金委员会 | Sensor signal processing method and sensor signal processing device |
US7826879B2 (en) | 2006-02-28 | 2010-11-02 | Abbott Diabetes Care Inc. | Analyte sensors and methods of use |
US7981034B2 (en) | 2006-02-28 | 2011-07-19 | Abbott Diabetes Care Inc. | Smart messages and alerts for an infusion delivery and management system |
US7630748B2 (en) | 2006-10-25 | 2009-12-08 | Abbott Diabetes Care Inc. | Method and system for providing analyte monitoring |
US9675290B2 (en) | 2012-10-30 | 2017-06-13 | Abbott Diabetes Care Inc. | Sensitivity calibration of in vivo sensors used to measure analyte concentration |
US8473022B2 (en) | 2008-01-31 | 2013-06-25 | Abbott Diabetes Care Inc. | Analyte sensor with time lag compensation |
US8140312B2 (en) | 2007-05-14 | 2012-03-20 | Abbott Diabetes Care Inc. | Method and system for determining analyte levels |
US8374668B1 (en) | 2007-10-23 | 2013-02-12 | Abbott Diabetes Care Inc. | Analyte sensor with lag compensation |
US9339217B2 (en) | 2011-11-25 | 2016-05-17 | Abbott Diabetes Care Inc. | Analyte monitoring system and methods of use |
US8583205B2 (en) * | 2008-03-28 | 2013-11-12 | Abbott Diabetes Care Inc. | Analyte sensor calibration management |
US9392969B2 (en) | 2008-08-31 | 2016-07-19 | Abbott Diabetes Care Inc. | Closed loop control and signal attenuation detection |
US8224415B2 (en) | 2009-01-29 | 2012-07-17 | Abbott Diabetes Care Inc. | Method and device for providing offset model based calibration for analyte sensor |
US8346335B2 (en) | 2008-03-28 | 2013-01-01 | Abbott Diabetes Care Inc. | Analyte sensor calibration management |
US8206296B2 (en) | 2006-08-07 | 2012-06-26 | Abbott Diabetes Care Inc. | Method and system for providing integrated analyte monitoring and infusion system therapy management |
US8932216B2 (en) | 2006-08-07 | 2015-01-13 | Abbott Diabetes Care Inc. | Method and system for providing data management in integrated analyte monitoring and infusion system |
US8930203B2 (en) | 2007-02-18 | 2015-01-06 | Abbott Diabetes Care Inc. | Multi-function analyte test device and methods therefor |
US8732188B2 (en) | 2007-02-18 | 2014-05-20 | Abbott Diabetes Care Inc. | Method and system for providing contextual based medication dosage determination |
EP2146627B1 (en) | 2007-04-14 | 2020-07-29 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in medical communication system |
ES2784736T3 (en) | 2007-04-14 | 2020-09-30 | Abbott Diabetes Care Inc | Procedure and apparatus for providing data processing and control in a medical communication system |
US9204827B2 (en) | 2007-04-14 | 2015-12-08 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in medical communication system |
EP2146625B1 (en) | 2007-04-14 | 2019-08-14 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in medical communication system |
US8103471B2 (en) | 2007-05-14 | 2012-01-24 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8444560B2 (en) | 2007-05-14 | 2013-05-21 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US10002233B2 (en) | 2007-05-14 | 2018-06-19 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8239166B2 (en) | 2007-05-14 | 2012-08-07 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8260558B2 (en) | 2007-05-14 | 2012-09-04 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8600681B2 (en) | 2007-05-14 | 2013-12-03 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8560038B2 (en) | 2007-05-14 | 2013-10-15 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US9125548B2 (en) | 2007-05-14 | 2015-09-08 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8160900B2 (en) | 2007-06-29 | 2012-04-17 | Abbott Diabetes Care Inc. | Analyte monitoring and management device and method to analyze the frequency of user interaction with the device |
US8834366B2 (en) | 2007-07-31 | 2014-09-16 | Abbott Diabetes Care Inc. | Method and apparatus for providing analyte sensor calibration |
US8216138B1 (en) | 2007-10-23 | 2012-07-10 | Abbott Diabetes Care Inc. | Correlation of alternative site blood and interstitial fluid glucose concentrations to venous glucose concentration |
US8377031B2 (en) | 2007-10-23 | 2013-02-19 | Abbott Diabetes Care Inc. | Closed loop control system with safety parameters and methods |
US8409093B2 (en) | 2007-10-23 | 2013-04-02 | Abbott Diabetes Care Inc. | Assessing measures of glycemic variability |
US20090164239A1 (en) | 2007-12-19 | 2009-06-25 | Abbott Diabetes Care, Inc. | Dynamic Display Of Glucose Information |
US8924159B2 (en) | 2008-05-30 | 2014-12-30 | Abbott Diabetes Care Inc. | Method and apparatus for providing glycemic control |
US8591410B2 (en) | 2008-05-30 | 2013-11-26 | Abbott Diabetes Care Inc. | Method and apparatus for providing glycemic control |
WO2010009172A1 (en) | 2008-07-14 | 2010-01-21 | Abbott Diabetes Care Inc. | Closed loop control system interface and methods |
US8734422B2 (en) | 2008-08-31 | 2014-05-27 | Abbott Diabetes Care Inc. | Closed loop control with improved alarm functions |
US8622988B2 (en) | 2008-08-31 | 2014-01-07 | Abbott Diabetes Care Inc. | Variable rate closed loop control and methods |
US20100057040A1 (en) | 2008-08-31 | 2010-03-04 | Abbott Diabetes Care, Inc. | Robust Closed Loop Control And Methods |
US9943644B2 (en) | 2008-08-31 | 2018-04-17 | Abbott Diabetes Care Inc. | Closed loop control with reference measurement and methods thereof |
US8986208B2 (en) | 2008-09-30 | 2015-03-24 | Abbott Diabetes Care Inc. | Analyte sensor sensitivity attenuation mitigation |
US9326707B2 (en) | 2008-11-10 | 2016-05-03 | Abbott Diabetes Care Inc. | Alarm characterization for analyte monitoring devices and systems |
WO2010054408A1 (en) * | 2008-11-10 | 2010-05-14 | Abbott Diabetes Care Inc. | Method and system for providing dropout detection in analyte sensors |
US8103456B2 (en) | 2009-01-29 | 2012-01-24 | Abbott Diabetes Care Inc. | Method and device for early signal attenuation detection using blood glucose measurements |
WO2010121084A1 (en) | 2009-04-15 | 2010-10-21 | Abbott Diabetes Care Inc. | Analyte monitoring system having an alert |
EP2421435A4 (en) * | 2009-04-20 | 2016-10-19 | Resmed Ltd | Discrimination of cheyne -stokes breathing patterns by use of oximetry signals |
EP2425209A4 (en) | 2009-04-29 | 2013-01-09 | Abbott Diabetes Care Inc | Method and system for providing real time analyte sensor calibration with retrospective backfill |
EP2438527B1 (en) | 2009-06-04 | 2018-05-02 | Abbott Diabetes Care, Inc. | Method and system for updating a medical device |
EP2456351B1 (en) | 2009-07-23 | 2016-10-12 | Abbott Diabetes Care, Inc. | Real time management of data relating to physiological control of glucose levels |
ES2776474T3 (en) | 2009-07-23 | 2020-07-30 | Abbott Diabetes Care Inc | Continuous analyte measurement system |
AU2010278894B2 (en) | 2009-07-30 | 2014-01-30 | Tandem Diabetes Care, Inc. | Infusion pump system with disposable cartridge having pressure venting and pressure feedback |
WO2011014851A1 (en) | 2009-07-31 | 2011-02-03 | Abbott Diabetes Care Inc. | Method and apparatus for providing analyte monitoring system calibration accuracy |
EP3988470B1 (en) | 2009-08-31 | 2023-06-28 | Abbott Diabetes Care Inc. | Displays for a medical device |
US9320461B2 (en) | 2009-09-29 | 2016-04-26 | Abbott Diabetes Care Inc. | Method and apparatus for providing notification function in analyte monitoring systems |
US8185181B2 (en) | 2009-10-30 | 2012-05-22 | Abbott Diabetes Care Inc. | Method and apparatus for detecting false hypoglycemic conditions |
WO2011112753A1 (en) | 2010-03-10 | 2011-09-15 | Abbott Diabetes Care Inc. | Systems, devices and methods for managing glucose levels |
US8635046B2 (en) | 2010-06-23 | 2014-01-21 | Abbott Diabetes Care Inc. | Method and system for evaluating analyte sensor response characteristics |
US10092229B2 (en) | 2010-06-29 | 2018-10-09 | Abbott Diabetes Care Inc. | Calibration of analyte measurement system |
WO2012048168A2 (en) | 2010-10-07 | 2012-04-12 | Abbott Diabetes Care Inc. | Analyte monitoring devices and methods |
US8454554B2 (en) | 2010-10-15 | 2013-06-04 | Roche Diagnostics Operations, Inc. | Use of a handheld medical device as a communications mediator between a personal computer-based configurator and another networked medical device |
US8861731B2 (en) | 2010-10-15 | 2014-10-14 | Roche Diagnostics Operations, Inc. | Efficient procedure for pairing medical devices for wireless communication with limited user interaction |
US8401194B2 (en) | 2010-10-15 | 2013-03-19 | Roche Diagnostics Operations, Inc. | Diabetes care kit that is preconfigured to establish a secure bidirectional communication link between a blood glucose meter and insulin pump |
US8589106B2 (en) | 2010-12-22 | 2013-11-19 | Roche Diagnostics Operations, Inc. | Calibration of a handheld diabetes managing device that receives data from a continuous glucose monitor |
US8672874B2 (en) | 2010-12-22 | 2014-03-18 | Roche Diagnoistics Operations, Inc. | Communication protocol that supports pass-thru communication |
US10136845B2 (en) | 2011-02-28 | 2018-11-27 | Abbott Diabetes Care Inc. | Devices, systems, and methods associated with analyte monitoring devices and devices incorporating the same |
WO2012142502A2 (en) | 2011-04-15 | 2012-10-18 | Dexcom Inc. | Advanced analyte sensor calibration and error detection |
US9622691B2 (en) | 2011-10-31 | 2017-04-18 | Abbott Diabetes Care Inc. | Model based variable risk false glucose threshold alarm prevention mechanism |
US9317656B2 (en) | 2011-11-23 | 2016-04-19 | Abbott Diabetes Care Inc. | Compatibility mechanisms for devices in a continuous analyte monitoring system and methods thereof |
US8710993B2 (en) | 2011-11-23 | 2014-04-29 | Abbott Diabetes Care Inc. | Mitigating single point failure of devices in an analyte monitoring system and methods thereof |
AU2012362155A1 (en) | 2011-12-30 | 2014-01-23 | Abbott Diabetes Care Inc. | Method and apparatus for determining medication dose information |
US9700253B2 (en) | 2012-03-16 | 2017-07-11 | Dexcom, Inc. | Systems and methods for processing analyte sensor data |
US9180242B2 (en) | 2012-05-17 | 2015-11-10 | Tandem Diabetes Care, Inc. | Methods and devices for multiple fluid transfer |
WO2014052136A1 (en) | 2012-09-26 | 2014-04-03 | Abbott Diabetes Care Inc. | Method and apparatus for improving lag correction during in vivo measurement of analyte concentration with analyte concentration variability and range data |
US9119529B2 (en) | 2012-10-30 | 2015-09-01 | Dexcom, Inc. | Systems and methods for dynamically and intelligently monitoring a host's glycemic condition after an alert is triggered |
US9173998B2 (en) | 2013-03-14 | 2015-11-03 | Tandem Diabetes Care, Inc. | System and method for detecting occlusions in an infusion pump |
US9474475B1 (en) | 2013-03-15 | 2016-10-25 | Abbott Diabetes Care Inc. | Multi-rate analyte sensor data collection with sample rate configurable signal processing |
US10076285B2 (en) | 2013-03-15 | 2018-09-18 | Abbott Diabetes Care Inc. | Sensor fault detection using analyte sensor data pattern comparison |
US9486171B2 (en) | 2013-03-15 | 2016-11-08 | Tandem Diabetes Care, Inc. | Predictive calibration |
US10433773B1 (en) | 2013-03-15 | 2019-10-08 | Abbott Diabetes Care Inc. | Noise rejection methods and apparatus for sparsely sampled analyte sensor data |
US11229382B2 (en) | 2013-12-31 | 2022-01-25 | Abbott Diabetes Care Inc. | Self-powered analyte sensor and devices using the same |
US20170185748A1 (en) | 2014-03-30 | 2017-06-29 | Abbott Diabetes Care Inc. | Method and Apparatus for Determining Meal Start and Peak Events in Analyte Monitoring Systems |
EP3319518A4 (en) | 2015-07-10 | 2019-03-13 | Abbott Diabetes Care Inc. | System, device and method of dynamic glucose profile response to physiological parameters |
US11596330B2 (en) | 2017-03-21 | 2023-03-07 | Abbott Diabetes Care Inc. | Methods, devices and system for providing diabetic condition diagnosis and therapy |
US11331022B2 (en) | 2017-10-24 | 2022-05-17 | Dexcom, Inc. | Pre-connected analyte sensors |
US11382540B2 (en) | 2017-10-24 | 2022-07-12 | Dexcom, Inc. | Pre-connected analyte sensors |
US11776684B2 (en) | 2019-09-26 | 2023-10-03 | Pacesetter, Inc | Method and device for managing energy usage by a medical device |
Family Cites Families (654)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB1191363A (en) | 1968-02-19 | 1970-05-13 | Pavelle Ltd | Improvements in or relating to Electronic Thermostats. |
US3949388A (en) | 1972-11-13 | 1976-04-06 | Monitron Industries, Inc. | Physiological sensor and transmitter |
US3926760A (en) | 1973-09-28 | 1975-12-16 | Du Pont | Process for electrophoretic deposition of polymer |
US3978596A (en) * | 1974-11-25 | 1976-09-07 | Brown Dwight C | Sandals and method of making same |
US4245634A (en) | 1975-01-22 | 1981-01-20 | Hospital For Sick Children | Artificial beta cell |
US3978856A (en) * | 1975-03-20 | 1976-09-07 | Michel Walter A | Heart beat waveform monitoring apparatus |
US4036749A (en) | 1975-04-30 | 1977-07-19 | Anderson Donald R | Purification of saline water |
US3960497A (en) | 1975-08-19 | 1976-06-01 | Beckman Instruments, Inc. | Chemical analyzer with automatic calibration |
US4055175A (en) | 1976-05-07 | 1977-10-25 | Miles Laboratories, Inc. | Blood glucose control apparatus |
US4129128A (en) | 1977-02-23 | 1978-12-12 | Mcfarlane Richard H | Securing device for catheter placement assembly |
US4344438A (en) | 1978-08-02 | 1982-08-17 | The United States Of America As Represented By The Department Of Health, Education And Welfare | Optical sensor of plasma constituents |
AU530979B2 (en) | 1978-12-07 | 1983-08-04 | Aus. Training Aids Pty. Ltd., | Detecting position of bullet fired at target |
US4373527B1 (en) * | 1979-04-27 | 1995-06-27 | Univ Johns Hopkins | Implantable programmable medication infusion system |
US4731051A (en) | 1979-04-27 | 1988-03-15 | The Johns Hopkins University | Programmable control means for providing safe and controlled medication infusion |
CS210174B1 (en) | 1979-07-12 | 1982-01-29 | Ivan Emmer | Method of making the electric hygrometric sensor |
US4425920A (en) | 1980-10-24 | 1984-01-17 | Purdue Research Foundation | Apparatus and method for measurement and control of blood pressure |
US4327725A (en) | 1980-11-25 | 1982-05-04 | Alza Corporation | Osmotic device with hydrogel driving member |
US4392849A (en) | 1981-07-27 | 1983-07-12 | The Cleveland Clinic Foundation | Infusion pump controller |
DE3138194A1 (en) | 1981-09-25 | 1983-04-14 | Basf Ag, 6700 Ludwigshafen | WATER-INSOLUBLE POROESES PROTEIN MATERIAL, THEIR PRODUCTION AND USE |
DE3278334D1 (en) | 1981-10-23 | 1988-05-19 | Genetics Int Inc | Sensor for components of a liquid mixture |
US4494950A (en) | 1982-01-19 | 1985-01-22 | The Johns Hopkins University | Plural module medication delivery system |
US4462048A (en) * | 1982-02-11 | 1984-07-24 | Rca Corporation | Noise reduction circuitry for audio signals |
FI831399L (en) | 1982-04-29 | 1983-10-30 | Agripat Sa | KONTAKTLINS AV HAERDAD POLYVINYL ALCOHOL |
EP0098592A3 (en) | 1982-07-06 | 1985-08-21 | Fujisawa Pharmaceutical Co., Ltd. | Portable artificial pancreas |
US4509531A (en) | 1982-07-28 | 1985-04-09 | Teledyne Industries, Inc. | Personal physiological monitor |
US4527240A (en) | 1982-12-29 | 1985-07-02 | Kvitash Vadim I | Balascopy method for detecting and rapidly evaluating multiple imbalances within multi-parametric systems |
CA1219040A (en) | 1983-05-05 | 1987-03-10 | Elliot V. Plotkin | Measurement of enzyme-catalysed reactions |
CA1226036A (en) | 1983-05-05 | 1987-08-25 | Irving J. Higgins | Analytical equipment and sensor electrodes therefor |
US5509410A (en) | 1983-06-06 | 1996-04-23 | Medisense, Inc. | Strip electrode including screen printing of a single layer |
US5682884A (en) | 1983-05-05 | 1997-11-04 | Medisense, Inc. | Strip electrode with screen printing |
US4538616A (en) | 1983-07-25 | 1985-09-03 | Robert Rogoff | Blood sugar level sensing and monitoring transducer |
DE3429596A1 (en) | 1984-08-10 | 1986-02-20 | Siemens AG, 1000 Berlin und 8000 München | DEVICE FOR THE PHYSIOLOGICAL FREQUENCY CONTROL OF A PACEMAKER PROVIDED WITH A PICTURE ELECTRODE |
CA1254091A (en) | 1984-09-28 | 1989-05-16 | Vladimir Feingold | Implantable medication infusion system |
US5279294A (en) | 1985-04-08 | 1994-01-18 | Cascade Medical, Inc. | Medical diagnostic system |
US4671288A (en) | 1985-06-13 | 1987-06-09 | The Regents Of The University Of California | Electrochemical cell sensor for continuous short-term use in tissues and blood |
US4890620A (en) | 1985-09-20 | 1990-01-02 | The Regents Of The University Of California | Two-dimensional diffusion glucose substrate sensing electrode |
US4759366A (en) | 1986-03-19 | 1988-07-26 | Telectronics N.V. | Rate responsive pacing using the ventricular gradient |
US4757022A (en) | 1986-04-15 | 1988-07-12 | Markwell Medical Institute, Inc. | Biological fluid measuring device |
US4703756A (en) | 1986-05-06 | 1987-11-03 | The Regents Of The University Of California | Complete glucose monitoring system with an implantable, telemetered sensor module |
US4731726A (en) | 1986-05-19 | 1988-03-15 | Healthware Corporation | Patient-operated glucose monitor and diabetes management system |
US5055171A (en) | 1986-10-06 | 1991-10-08 | T And G Corporation | Ionic semiconductor materials and applications thereof |
US4777953A (en) | 1987-02-25 | 1988-10-18 | Ash Medical Systems, Inc. | Capillary filtration and collection method for long-term monitoring of blood constituents |
US5002054A (en) | 1987-02-25 | 1991-03-26 | Ash Medical Systems, Inc. | Interstitial filtration and collection device and method for long-term monitoring of physiological constituents of the body |
US4854322A (en) | 1987-02-25 | 1989-08-08 | Ash Medical Systems, Inc. | Capillary filtration and collection device for long-term monitoring of blood constituents |
US5365426A (en) | 1987-03-13 | 1994-11-15 | The University Of Maryland | Advanced signal processing methodology for the detection, localization and quantification of acute myocardial ischemia |
US4759828A (en) | 1987-04-09 | 1988-07-26 | Nova Biomedical Corporation | Glucose electrode and method of determining glucose |
US4749985A (en) | 1987-04-13 | 1988-06-07 | United States Of America As Represented By The United States Department Of Energy | Functional relationship-based alarm processing |
EP0290683A3 (en) | 1987-05-01 | 1988-12-14 | Diva Medical Systems B.V. | Diabetes management system and apparatus |
GB8725936D0 (en) | 1987-11-05 | 1987-12-09 | Genetics Int Inc | Sensing system |
US4925268A (en) | 1988-07-25 | 1990-05-15 | Abbott Laboratories | Fiber-optic physiological probes |
EP0353328A1 (en) | 1988-08-03 | 1990-02-07 | Dräger Nederland B.V. | A polarographic-amperometric three-electrode sensor |
US5340722A (en) | 1988-08-24 | 1994-08-23 | Avl Medical Instruments Ag | Method for the determination of the concentration of an enzyme substrate and a sensor for carrying out the method |
US4995402A (en) | 1988-10-12 | 1991-02-26 | Thorne, Smith, Astill Technologies, Inc. | Medical droplet whole blood and like monitoring |
US5360404A (en) | 1988-12-14 | 1994-11-01 | Inviro Medical Devices Ltd. | Needle guard and needle assembly for syringe |
US4947845A (en) | 1989-01-13 | 1990-08-14 | Pacesetter Infusion, Ltd. | Method of maximizing catheter longevity in an implantable medication infusion system |
US5068536A (en) | 1989-01-19 | 1991-11-26 | Futrex, Inc. | Method for providing custom calibration for near infrared instruments for measurement of blood glucose |
US5077476A (en) | 1990-06-27 | 1991-12-31 | Futrex, Inc. | Instrument for non-invasive measurement of blood glucose |
DK0385805T3 (en) | 1989-03-03 | 1996-09-23 | Edward W Stark | Method and apparatus for signal processing |
JPH02298855A (en) | 1989-03-20 | 1990-12-11 | Assoc Univ Inc | Electrochemical biosensor using immobilized enzyme and redox polymer |
US4953552A (en) | 1989-04-21 | 1990-09-04 | Demarzo Arthur P | Blood glucose monitoring system |
EP0396788A1 (en) | 1989-05-08 | 1990-11-14 | Dräger Nederland B.V. | Process and sensor for measuring the glucose content of glucosecontaining fluids |
FR2648353B1 (en) | 1989-06-16 | 1992-03-27 | Europhor Sa | MICRODIALYSIS PROBE |
US5431160A (en) | 1989-07-19 | 1995-07-11 | University Of New Mexico | Miniature implantable refillable glucose sensor and material therefor |
US4986271A (en) | 1989-07-19 | 1991-01-22 | The University Of New Mexico | Vivo refillable glucose sensor |
US5264105A (en) | 1989-08-02 | 1993-11-23 | Gregg Brian A | Enzyme electrodes |
US5264104A (en) | 1989-08-02 | 1993-11-23 | Gregg Brian A | Enzyme electrodes |
US5320725A (en) | 1989-08-02 | 1994-06-14 | E. Heller & Company | Electrode and method for the detection of hydrogen peroxide |
US5262035A (en) | 1989-08-02 | 1993-11-16 | E. Heller And Company | Enzyme electrodes |
US5568400A (en) | 1989-09-01 | 1996-10-22 | Stark; Edward W. | Multiplicative signal correction method and apparatus |
US5050612A (en) | 1989-09-12 | 1991-09-24 | Matsumura Kenneth N | Device for computer-assisted monitoring of the body |
US5082550A (en) | 1989-12-11 | 1992-01-21 | The United States Of America As Represented By The Department Of Energy | Enzyme electrochemical sensor electrode and method of making it |
US5342789A (en) | 1989-12-14 | 1994-08-30 | Sensor Technologies, Inc. | Method and device for detecting and quantifying glucose in body fluids |
US5165407A (en) | 1990-04-19 | 1992-11-24 | The University Of Kansas | Implantable glucose sensor |
GB2243211A (en) | 1990-04-20 | 1991-10-23 | Philips Electronic Associated | Analytical instrument and method of calibrating an analytical instrument |
US5202261A (en) | 1990-07-19 | 1993-04-13 | Miles Inc. | Conductive sensors and their use in diagnostic assays |
US5113869A (en) | 1990-08-21 | 1992-05-19 | Telectronics Pacing Systems, Inc. | Implantable ambulatory electrocardiogram monitor |
EP0550641B1 (en) | 1990-09-28 | 1994-05-25 | Pfizer Inc. | Dispensing device containing a hydrophobic medium |
ATE155575T1 (en) | 1990-12-12 | 1997-08-15 | Sherwood Medical Co | CALIBRATION OF AN INFRARED THERMOMETER USING AREA CALIBRATION CURVE REPRESENTATION |
US5148812A (en) | 1991-02-20 | 1992-09-22 | Georgetown University | Non-invasive dynamic tracking of cardiac vulnerability by analysis of t-wave alternans |
JPH04278450A (en) | 1991-03-04 | 1992-10-05 | Adam Heller | Biosensor and method for analyzing subject |
US5262305A (en) | 1991-03-04 | 1993-11-16 | E. Heller & Company | Interferant eliminating biosensors |
US5593852A (en) * | 1993-12-02 | 1997-01-14 | Heller; Adam | Subcutaneous glucose electrode |
US5469855A (en) | 1991-03-08 | 1995-11-28 | Exergen Corporation | Continuous temperature monitor |
US5135004A (en) | 1991-03-12 | 1992-08-04 | Incontrol, Inc. | Implantable myocardial ischemia monitor and related method |
US5204264A (en) | 1991-03-14 | 1993-04-20 | E. I. Du Pont De Nemours And Company | Method for validation of calibration standards in an automatic chemical analyzer |
US5199428A (en) | 1991-03-22 | 1993-04-06 | Medtronic, Inc. | Implantable electrical nerve stimulator/pacemaker with ischemia for decreasing cardiac workload |
US5122925A (en) | 1991-04-22 | 1992-06-16 | Control Products, Inc. | Package for electronic components |
US5868711A (en) | 1991-04-29 | 1999-02-09 | Board Of Regents, The University Of Texas System | Implantable intraosseous device for rapid vascular access |
US5328460A (en) | 1991-06-21 | 1994-07-12 | Pacesetter Infusion, Ltd. | Implantable medication infusion pump including self-contained acoustic fault detection apparatus |
US5231988A (en) | 1991-08-09 | 1993-08-03 | Cyberonics, Inc. | Treatment of endocrine disorders by nerve stimulation |
GB9120144D0 (en) | 1991-09-20 | 1991-11-06 | Imperial College | A dialysis electrode device |
US5322063A (en) | 1991-10-04 | 1994-06-21 | Eli Lilly And Company | Hydrophilic polyurethane membranes for electrochemical glucose sensors |
US5203326A (en) | 1991-12-18 | 1993-04-20 | Telectronics Pacing Systems, Inc. | Antiarrhythmia pacer using antiarrhythmia pacing and autonomic nerve stimulation therapy |
US5372427A (en) | 1991-12-19 | 1994-12-13 | Texas Instruments Incorporated | Temperature sensor |
US5285792A (en) | 1992-01-10 | 1994-02-15 | Physio-Control Corporation | System for producing prioritized alarm messages in a medical instrument |
US5313953A (en) | 1992-01-14 | 1994-05-24 | Incontrol, Inc. | Implantable cardiac patient monitor |
US5246867A (en) | 1992-01-17 | 1993-09-21 | University Of Maryland At Baltimore | Determination and quantification of saccharides by luminescence lifetimes and energy transfer |
IL104365A0 (en) | 1992-01-31 | 1993-05-13 | Gensia Pharma | Method and apparatus for closed loop drug delivery |
US5328927A (en) | 1992-03-03 | 1994-07-12 | Merck Sharpe & Dohme, Ltd. | Hetercyclic compounds, processes for their preparation and pharmaceutical compositions containing them |
US5711001A (en) | 1992-05-08 | 1998-01-20 | Motorola, Inc. | Method and circuit for acquisition by a radio receiver |
GB9211402D0 (en) | 1992-05-29 | 1992-07-15 | Univ Manchester | Sensor devices |
DK95792A (en) | 1992-07-24 | 1994-01-25 | Radiometer As | Sensor for non-invasive, in vivo determination of an analyte and blood flow |
US5330634A (en) | 1992-08-28 | 1994-07-19 | Via Medical Corporation | Calibration solutions useful for analyses of biological fluids and methods employing same |
US6283761B1 (en) | 1992-09-08 | 2001-09-04 | Raymond Anthony Joao | Apparatus and method for processing and/or for providing healthcare information and/or healthcare-related information |
US5376070A (en) | 1992-09-29 | 1994-12-27 | Minimed Inc. | Data transfer system for an infusion pump |
WO1994010553A1 (en) | 1992-10-23 | 1994-05-11 | Optex Biomedical, Inc. | Fibre-optic probe for the measurement of fluid parameters |
US5899855A (en) | 1992-11-17 | 1999-05-04 | Health Hero Network, Inc. | Modular microprocessor-based health monitoring system |
US5956501A (en) | 1997-01-10 | 1999-09-21 | Health Hero Network, Inc. | Disease simulation system and method |
US5601435A (en) | 1994-11-04 | 1997-02-11 | Intercare | Method and apparatus for interactively monitoring a physiological condition and for interactively providing health related information |
US5918603A (en) | 1994-05-23 | 1999-07-06 | Health Hero Network, Inc. | Method for treating medical conditions using a microprocessor-based video game |
ZA938555B (en) | 1992-11-23 | 1994-08-02 | Lilly Co Eli | Technique to improve the performance of electrochemical sensors |
US5299571A (en) | 1993-01-22 | 1994-04-05 | Eli Lilly And Company | Apparatus and method for implantation of sensors |
EP1130383B1 (en) | 1993-04-23 | 2004-09-15 | Roche Diagnostics GmbH | Diskette with circular arranged test elements |
US5384547A (en) * | 1993-08-02 | 1995-01-24 | Motorola, Inc. | Apparatus and method for attenuating a multicarrier input signal of a linear device |
DE4329898A1 (en) | 1993-09-04 | 1995-04-06 | Marcus Dr Besson | Wireless medical diagnostic and monitoring device |
US5438983A (en) | 1993-09-13 | 1995-08-08 | Hewlett-Packard Company | Patient alarm detection using trend vector analysis |
US5425749A (en) | 1993-09-16 | 1995-06-20 | Angeion Corporation | Preemptive cardioversion therapy in an implantable cardioverter defibrillator |
US5582184A (en) | 1993-10-13 | 1996-12-10 | Integ Incorporated | Interstitial fluid collection and constituent measurement |
US5400795A (en) | 1993-10-22 | 1995-03-28 | Telectronics Pacing Systems, Inc. | Method of classifying heart rhythms by analyzing several morphology defining metrics derived for a patient's QRS complex |
US5497772A (en) | 1993-11-19 | 1996-03-12 | Alfred E. Mann Foundation For Scientific Research | Glucose monitoring system |
US5791344A (en) | 1993-11-19 | 1998-08-11 | Alfred E. Mann Foundation For Scientific Research | Patient monitoring system |
US5320715A (en) | 1994-01-14 | 1994-06-14 | Lloyd Berg | Separation of 1-pentanol from cyclopentanol by extractive distillation |
DE4401400A1 (en) | 1994-01-19 | 1995-07-20 | Ernst Prof Dr Pfeiffer | Method and arrangement for continuously monitoring the concentration of a metabolite |
US5543326A (en) | 1994-03-04 | 1996-08-06 | Heller; Adam | Biosensor including chemically modified enzymes |
US5536249A (en) | 1994-03-09 | 1996-07-16 | Visionary Medical Products, Inc. | Pen-type injector with a microprocessor and blood characteristic monitor |
US5391250A (en) | 1994-03-15 | 1995-02-21 | Minimed Inc. | Method of fabricating thin film sensors |
US5390671A (en) | 1994-03-15 | 1995-02-21 | Minimed Inc. | Transcutaneous sensor insertion set |
US5609575A (en) | 1994-04-11 | 1997-03-11 | Graseby Medical Limited | Infusion pump and method with dose-rate calculation |
US5569186A (en) | 1994-04-25 | 1996-10-29 | Minimed Inc. | Closed loop infusion pump system with removable glucose sensor |
DE4415896A1 (en) | 1994-05-05 | 1995-11-09 | Boehringer Mannheim Gmbh | Analysis system for monitoring the concentration of an analyte in the blood of a patient |
US5472317A (en) | 1994-06-03 | 1995-12-05 | Minimed Inc. | Mounting clip for a medication infusion pump |
US5520191A (en) | 1994-10-07 | 1996-05-28 | Ortivus Medical Ab | Myocardial ischemia and infarction analysis and monitoring method and apparatus |
US6038469A (en) | 1994-10-07 | 2000-03-14 | Ortivus Ab | Myocardial ischemia and infarction analysis and monitoring method and apparatus |
US5568806A (en) | 1995-02-16 | 1996-10-29 | Minimed Inc. | Transcutaneous sensor insertion set |
US5586553A (en) | 1995-02-16 | 1996-12-24 | Minimed Inc. | Transcutaneous sensor insertion set |
US5752512A (en) | 1995-05-10 | 1998-05-19 | Massachusetts Institute Of Technology | Apparatus and method for non-invasive blood analyte measurement |
US5628310A (en) | 1995-05-19 | 1997-05-13 | Joseph R. Lakowicz | Method and apparatus to perform trans-cutaneous analyte monitoring |
US5995860A (en) | 1995-07-06 | 1999-11-30 | Thomas Jefferson University | Implantable sensor and system for measurement and control of blood constituent levels |
US7016713B2 (en) | 1995-08-09 | 2006-03-21 | Inlight Solutions, Inc. | Non-invasive determination of direction and rate of change of an analyte |
US5628890A (en) | 1995-09-27 | 1997-05-13 | Medisense, Inc. | Electrochemical sensor |
US5972199A (en) | 1995-10-11 | 1999-10-26 | E. Heller & Company | Electrochemical analyte sensors using thermostable peroxidase |
US5665222A (en) | 1995-10-11 | 1997-09-09 | E. Heller & Company | Soybean peroxidase electrochemical sensor |
US5741211A (en) | 1995-10-26 | 1998-04-21 | Medtronic, Inc. | System and method for continuous monitoring of diabetes-related blood constituents |
US5711861A (en) | 1995-11-22 | 1998-01-27 | Ward; W. Kenneth | Device for monitoring changes in analyte concentration |
FI960636A (en) | 1996-02-12 | 1997-08-13 | Nokia Mobile Phones Ltd | A procedure for monitoring the health of a patient |
US5785660A (en) | 1996-03-28 | 1998-07-28 | Pacesetter, Inc. | Methods and apparatus for storing intracardiac electrograms |
DE19618597B4 (en) | 1996-05-09 | 2005-07-21 | Institut für Diabetestechnologie Gemeinnützige Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm | Method for determining the concentration of tissue glucose |
US20040249420A1 (en) | 1996-05-14 | 2004-12-09 | Medtronic, Inc. | Prioritized rule based method and apparatus for diagnosis and treatment of arrhythmias |
US5735285A (en) | 1996-06-04 | 1998-04-07 | Data Critical Corp. | Method and hand-held apparatus for demodulating and viewing frequency modulated biomedical signals |
ATE234129T1 (en) | 1996-06-18 | 2003-03-15 | Alza Corp | DEVICE FOR IMPROVING TRANSDERMAL ADMINISTRATION OF MEDICATIONS OR EXTRACTION OF BODY FLUID |
AU3596597A (en) | 1996-07-08 | 1998-02-02 | Animas Corporation | Implantable sensor and system for in vivo measurement and control of fluid constituent levels |
US6544193B2 (en) * | 1996-09-04 | 2003-04-08 | Marcio Marc Abreu | Noninvasive measurement of chemical substances |
US5720295A (en) | 1996-10-15 | 1998-02-24 | Pacesetter, Inc. | Pacemaker with improved detection of atrial fibrillation |
US6071249A (en) | 1996-12-06 | 2000-06-06 | Abbott Laboratories | Method and apparatus for obtaining blood for diagnostic tests |
US5964993A (en) | 1996-12-19 | 1999-10-12 | Implanted Biosystems Inc. | Glucose sensor |
US5914026A (en) | 1997-01-06 | 1999-06-22 | Implanted Biosystems Inc. | Implantable sensor employing an auxiliary electrode |
US6122351A (en) | 1997-01-21 | 2000-09-19 | Med Graph, Inc. | Method and system aiding medical diagnosis and treatment |
SE9700181D0 (en) | 1997-01-22 | 1997-01-22 | Pacesetter Ab | Ischemia detector and heart stimulator provided with such an ischemia detector |
SE9700182D0 (en) | 1997-01-22 | 1997-01-22 | Pacesetter Ab | Implantable heart stimulator |
US6093172A (en) | 1997-02-05 | 2000-07-25 | Minimed Inc. | Injector for a subcutaneous insertion set |
US6607509B2 (en) | 1997-12-31 | 2003-08-19 | Medtronic Minimed, Inc. | Insertion device for an insertion set and method of using the same |
ATE227844T1 (en) | 1997-02-06 | 2002-11-15 | Therasense Inc | SMALL VOLUME SENSOR FOR IN-VITRO DETERMINATION |
SE9700427D0 (en) | 1997-02-07 | 1997-02-07 | Pacesetter Ab | Ischemia detector |
WO1998037805A1 (en) | 1997-02-26 | 1998-09-03 | Diasense, Inc. | Individual calibration of blood glucose for supporting noninvasive self-monitoring blood glucose |
US6159147A (en) | 1997-02-28 | 2000-12-12 | Qrs Diagnostics, Llc | Personal computer card for collection of real-time biological data |
US20050033132A1 (en) | 1997-03-04 | 2005-02-10 | Shults Mark C. | Analyte measuring device |
US6741877B1 (en) | 1997-03-04 | 2004-05-25 | Dexcom, Inc. | Device and method for determining analyte levels |
US7657297B2 (en) | 2004-05-03 | 2010-02-02 | Dexcom, Inc. | Implantable analyte sensor |
US7192450B2 (en) | 2003-05-21 | 2007-03-20 | Dexcom, Inc. | Porous membranes for use with implantable devices |
US6862465B2 (en) | 1997-03-04 | 2005-03-01 | Dexcom, Inc. | Device and method for determining analyte levels |
US6558321B1 (en) | 1997-03-04 | 2003-05-06 | Dexcom, Inc. | Systems and methods for remote monitoring and modulation of medical devices |
US7899511B2 (en) | 2004-07-13 | 2011-03-01 | Dexcom, Inc. | Low oxygen in vivo analyte sensor |
US6001067A (en) | 1997-03-04 | 1999-12-14 | Shults; Mark C. | Device and method for determining analyte levels |
US5891047A (en) | 1997-03-14 | 1999-04-06 | Cambridge Heart, Inc. | Detecting abnormal activation of heart |
US5792065A (en) | 1997-03-18 | 1998-08-11 | Marquette Medical Systems, Inc. | Method and apparatus for determining T-wave marker points during QT dispersion analysis |
SE9701121D0 (en) | 1997-03-26 | 1997-03-26 | Pacesetter Ab | Implantable heart stimulator |
SE9701122D0 (en) | 1997-03-26 | 1997-03-26 | Pacesetter Ab | Medical implant |
US6270455B1 (en) | 1997-03-28 | 2001-08-07 | Health Hero Network, Inc. | Networked system for interactive communications and remote monitoring of drug delivery |
US5942979A (en) | 1997-04-07 | 1999-08-24 | Luppino; Richard | On guard vehicle safety warning system |
US5961451A (en) | 1997-04-07 | 1999-10-05 | Motorola, Inc. | Noninvasive apparatus having a retaining member to retain a removable biosensor |
US5935224A (en) | 1997-04-24 | 1999-08-10 | Microsoft Corporation | Method and apparatus for adaptively coupling an external peripheral device to either a universal serial bus port on a computer or hub or a game port on a computer |
US7267665B2 (en) | 1999-06-03 | 2007-09-11 | Medtronic Minimed, Inc. | Closed loop system for controlling insulin infusion |
US6558351B1 (en) | 1999-06-03 | 2003-05-06 | Medtronic Minimed, Inc. | Closed loop system for controlling insulin infusion |
US5954643A (en) | 1997-06-09 | 1999-09-21 | Minimid Inc. | Insertion set for a transcutaneous sensor |
AU8031898A (en) | 1997-06-16 | 1999-01-04 | Elan Medical Technologies Limited | Methods of calibrating and testing a sensor for (in vivo) measurement of an analyte and devices for use in such methods |
US6764581B1 (en) | 1997-09-05 | 2004-07-20 | Abbott Laboratories | Electrode with thin working layer |
US6071391A (en) | 1997-09-12 | 2000-06-06 | Nok Corporation | Enzyme electrode structure |
US6117290A (en) | 1997-09-26 | 2000-09-12 | Pepex Biomedical, Llc | System and method for measuring a bioanalyte such as lactate |
US5904671A (en) | 1997-10-03 | 1999-05-18 | Navot; Nir | Tampon wetness detection system |
US6736957B1 (en) | 1997-10-16 | 2004-05-18 | Abbott Laboratories | Biosensor electrode mediators for regeneration of cofactors and process for using |
US6119028A (en) | 1997-10-20 | 2000-09-12 | Alfred E. Mann Foundation | Implantable enzyme-based monitoring systems having improved longevity due to improved exterior surfaces |
US6088608A (en) | 1997-10-20 | 2000-07-11 | Alfred E. Mann Foundation | Electrochemical sensor and integrity tests therefor |
FI107080B (en) | 1997-10-27 | 2001-05-31 | Nokia Mobile Phones Ltd | measuring device |
US6044285A (en) | 1997-11-12 | 2000-03-28 | Lightouch Medical, Inc. | Method for non-invasive measurement of an analyte |
US6579690B1 (en) | 1997-12-05 | 2003-06-17 | Therasense, Inc. | Blood analyte monitoring through subcutaneous measurement |
US6073031A (en) | 1997-12-24 | 2000-06-06 | Nortel Networks Corporation | Desktop docking station for use with a wireless telephone handset |
WO1999033504A1 (en) | 1997-12-31 | 1999-07-08 | Minimed Inc. | Insertion device for an insertion set and method of using the same |
US6103033A (en) | 1998-03-04 | 2000-08-15 | Therasense, Inc. | Process for producing an electrochemical biosensor |
US6134461A (en) | 1998-03-04 | 2000-10-17 | E. Heller & Company | Electrochemical analyte |
US6024699A (en) | 1998-03-13 | 2000-02-15 | Healthware Corporation | Systems, methods and computer program products for monitoring, diagnosing and treating medical conditions of remotely located patients |
US6197181B1 (en) | 1998-03-20 | 2001-03-06 | Semitool, Inc. | Apparatus and method for electrolytically depositing a metal on a microelectronic workpiece |
US6721582B2 (en) | 1999-04-06 | 2004-04-13 | Argose, Inc. | Non-invasive tissue glucose level monitoring |
JPH11296598A (en) | 1998-04-07 | 1999-10-29 | Seizaburo Arita | System and method for predicting blood-sugar level and record medium where same method is recorded |
US7647237B2 (en) | 1998-04-29 | 2010-01-12 | Minimed, Inc. | Communication station and software for interfacing with an infusion pump, analyte monitor, analyte meter, or the like |
US8974386B2 (en) | 1998-04-30 | 2015-03-10 | Abbott Diabetes Care Inc. | Analyte monitoring device and methods of use |
US6949816B2 (en) | 2003-04-21 | 2005-09-27 | Motorola, Inc. | Semiconductor component having first surface area for electrically coupling to a semiconductor chip and second surface area for electrically coupling to a substrate, and method of manufacturing same |
US6175752B1 (en) | 1998-04-30 | 2001-01-16 | Therasense, Inc. | Analyte monitoring device and methods of use |
GB2337122B (en) | 1998-05-08 | 2002-11-13 | Medisense Inc | Test strip |
US6233471B1 (en) | 1998-05-13 | 2001-05-15 | Cygnus, Inc. | Signal processing for measurement of physiological analysis |
WO1999058051A1 (en) | 1998-05-13 | 1999-11-18 | Cygnus, Inc. | Monitoring of physiological analytes |
US7043287B1 (en) | 1998-05-18 | 2006-05-09 | Abbott Laboratories | Method for modulating light penetration depth in tissue and diagnostic applications using same |
US6121611A (en) | 1998-05-20 | 2000-09-19 | Molecular Imaging Corporation | Force sensing probe for scanning probe microscopy |
US6223283B1 (en) | 1998-07-17 | 2001-04-24 | Compaq Computer Corporation | Method and apparatus for identifying display monitor functionality and compatibility |
US6115622A (en) | 1998-08-06 | 2000-09-05 | Medtronic, Inc. | Ambulatory recorder having enhanced sampling technique |
JP2002522103A (en) | 1998-08-07 | 2002-07-23 | インフィニット バイオメディカル テクノロジーズ インコーポレイテッド | Method for detecting, indicating and operating implantable myocardial ischemia |
US6558320B1 (en) | 2000-01-20 | 2003-05-06 | Medtronic Minimed, Inc. | Handheld personal data assistant (PDA) with a medical device and method of using the same |
US6248067B1 (en) | 1999-02-05 | 2001-06-19 | Minimed Inc. | Analyte sensor and holter-type monitor system and method of using the same |
US6740518B1 (en) | 1998-09-17 | 2004-05-25 | Clinical Micro Sensors, Inc. | Signal detection techniques for the detection of analytes |
US6254586B1 (en) | 1998-09-25 | 2001-07-03 | Minimed Inc. | Method and kit for supplying a fluid to a subcutaneous placement site |
WO2000018289A1 (en) * | 1998-09-30 | 2000-04-06 | Cygnus, Inc. | Method and device for predicting physiological values |
US6338790B1 (en) | 1998-10-08 | 2002-01-15 | Therasense, Inc. | Small volume in vitro analyte sensor with diffusible or non-leachable redox mediator |
US6591125B1 (en) | 2000-06-27 | 2003-07-08 | Therasense, Inc. | Small volume in vitro analyte sensor with diffusible or non-leachable redox mediator |
CA2345043C (en) | 1998-10-08 | 2009-08-11 | Minimed, Inc. | Telemetered characteristic monitor system |
US6602469B1 (en) | 1998-11-09 | 2003-08-05 | Lifestream Technologies, Inc. | Health monitoring and diagnostic device and network-based health assessment and medical records maintenance system |
CA2351734A1 (en) | 1998-11-20 | 2000-06-02 | University Of Connecticut | Generic integrated implantable potentiostat telemetry unit for electrochemical sensors |
US6773671B1 (en) | 1998-11-30 | 2004-08-10 | Abbott Laboratories | Multichemistry measuring device and test strips |
US6656114B1 (en) | 1998-11-30 | 2003-12-02 | Novo Noadisk A/S | Method and a system for assisting a user in a medical self treatment, said self treatment comprising a plurality of actions |
CA2351398A1 (en) | 1998-11-30 | 2000-06-08 | Abbott Laboratories | Analyte test instrument having improved calibration and communication processes |
US6161095A (en) | 1998-12-16 | 2000-12-12 | Health Hero Network, Inc. | Treatment regimen compliance and efficacy with feedback |
US7436511B2 (en) | 1999-01-22 | 2008-10-14 | Sensys Medical, Inc. | Analyte filter method and apparatus |
CA2365609A1 (en) | 1999-02-12 | 2000-08-17 | Cygnus, Inc. | Devices and methods for frequent measurement of an analyte present in a biological system |
US6112116A (en) | 1999-02-22 | 2000-08-29 | Cathco, Inc. | Implantable responsive system for sensing and treating acute myocardial infarction |
US6360888B1 (en) | 1999-02-25 | 2002-03-26 | Minimed Inc. | Glucose sensor package system |
US6424847B1 (en) | 1999-02-25 | 2002-07-23 | Medtronic Minimed, Inc. | Glucose monitor calibration methods |
US6272379B1 (en) | 1999-03-17 | 2001-08-07 | Cathco, Inc. | Implantable electronic system with acute myocardial infarction detection and patient warning capabilities |
US6128526A (en) | 1999-03-29 | 2000-10-03 | Medtronic, Inc. | Method for ischemia detection and apparatus for using same |
US6115628A (en) | 1999-03-29 | 2000-09-05 | Medtronic, Inc. | Method and apparatus for filtering electrocardiogram (ECG) signals to remove bad cycle information and for use of physiologic signals determined from said filtered ECG signals |
GB9907815D0 (en) | 1999-04-06 | 1999-06-02 | Univ Cambridge Tech | Implantable sensor |
US6285897B1 (en) * | 1999-04-07 | 2001-09-04 | Endonetics, Inc. | Remote physiological monitoring system |
US6200265B1 (en) | 1999-04-16 | 2001-03-13 | Medtronic, Inc. | Peripheral memory patch and access method for use with an implantable medical device |
US6108577A (en) | 1999-04-26 | 2000-08-22 | Cardiac Pacemakers, Inc. | Method and apparatus for detecting changes in electrocardiogram signals |
US6669663B1 (en) | 1999-04-30 | 2003-12-30 | Medtronic, Inc. | Closed loop medicament pump |
US6359444B1 (en) | 1999-05-28 | 2002-03-19 | University Of Kentucky Research Foundation | Remote resonant-circuit analyte sensing apparatus with sensing structure and associated method of sensing |
US7806886B2 (en) | 1999-06-03 | 2010-10-05 | Medtronic Minimed, Inc. | Apparatus and method for controlling insulin infusion with state variable feedback |
GB2351153B (en) | 1999-06-18 | 2003-03-26 | Abbott Lab | Electrochemical sensor for analysis of liquid samples |
US6423035B1 (en) | 1999-06-18 | 2002-07-23 | Animas Corporation | Infusion pump with a sealed drive mechanism and improved method of occlusion detection |
EP1192269A2 (en) | 1999-06-18 | 2002-04-03 | Therasense, Inc. | MASS TRANSPORT LIMITED i IN VIVO /i ANALYTE SENSOR |
US6413393B1 (en) | 1999-07-07 | 2002-07-02 | Minimed, Inc. | Sensor including UV-absorbing polymer and method of manufacture |
US6514460B1 (en) | 1999-07-28 | 2003-02-04 | Abbott Laboratories | Luminous glucose monitoring device |
US6471689B1 (en) | 1999-08-16 | 2002-10-29 | Thomas Jefferson University | Implantable drug delivery catheter system with capillary interface |
US6923763B1 (en) | 1999-08-23 | 2005-08-02 | University Of Virginia Patent Foundation | Method and apparatus for predicting the risk of hypoglycemia |
US7113821B1 (en) | 1999-08-25 | 2006-09-26 | Johnson & Johnson Consumer Companies, Inc. | Tissue electroperforation for enhanced drug delivery |
US6343225B1 (en) | 1999-09-14 | 2002-01-29 | Implanted Biosystems, Inc. | Implantable glucose sensor |
AT408182B (en) | 1999-09-17 | 2001-09-25 | Schaupp Lukas Dipl Ing Dr Tech | DEVICE FOR VIVO MEASURING SIZES IN LIVING ORGANISMS |
EP1217942A1 (en) | 1999-09-24 | 2002-07-03 | Healthetech, Inc. | Physiological monitor and associated computation, display and communication unit |
WO2001028495A2 (en) | 1999-10-08 | 2001-04-26 | Healthetech, Inc. | Indirect calorimeter for weight control |
US7317938B2 (en) * | 1999-10-08 | 2008-01-08 | Sensys Medical, Inc. | Method of adapting in-vitro models to aid in noninvasive glucose determination |
US6249705B1 (en) | 1999-10-21 | 2001-06-19 | Pacesetter, Inc. | Distributed network system for use with implantable medical devices |
US20060091006A1 (en) | 1999-11-04 | 2006-05-04 | Yi Wang | Analyte sensor with insertion monitor, and methods |
US6616819B1 (en) | 1999-11-04 | 2003-09-09 | Therasense, Inc. | Small volume in vitro analyte sensor and methods |
AU1602601A (en) | 1999-11-15 | 2001-05-30 | Therasense, Inc. | Polymeric transition metal complexes and uses thereof |
US6658396B1 (en) | 1999-11-29 | 2003-12-02 | Tang Sharon S | Neural network drug dosage estimation |
US6377852B1 (en) | 2000-01-20 | 2002-04-23 | Pacesetter, Inc. | Implanatable cardiac stimulation device and method for prolonging atrial refractoriness |
US7369635B2 (en) | 2000-01-21 | 2008-05-06 | Medtronic Minimed, Inc. | Rapid discrimination preambles and methods for using the same |
WO2001054753A2 (en) | 2000-01-21 | 2001-08-02 | Medical Research Group, Inc. | Microprocessor controlled ambulatory medical apparatus with hand held communication device |
EP1248661B1 (en) | 2000-01-21 | 2012-08-22 | Medtronic MiniMed, Inc. | Ambulatory medical apparatus and method having telemetry modifiable control software |
US6427088B1 (en) | 2000-01-21 | 2002-07-30 | Medtronic Minimed, Inc. | Ambulatory medical apparatus and method using telemetry system with predefined reception listening periods |
US7003336B2 (en) * | 2000-02-10 | 2006-02-21 | Medtronic Minimed, Inc. | Analyte sensor method of making the same |
US7890295B2 (en) | 2000-02-23 | 2011-02-15 | Medtronic Minimed, Inc. | Real time self-adjusting calibration algorithm |
US6895263B2 (en) | 2000-02-23 | 2005-05-17 | Medtronic Minimed, Inc. | Real time self-adjusting calibration algorithm |
US6572542B1 (en) | 2000-03-03 | 2003-06-03 | Medtronic, Inc. | System and method for monitoring and controlling the glycemic state of a patient |
US6405066B1 (en) | 2000-03-17 | 2002-06-11 | The Regents Of The University Of California | Implantable analyte sensor |
CA2404262C (en) | 2000-03-29 | 2009-03-24 | University Of Virginia Patent Foundation | Method, system, and computer program product for the evaluation of glycemic control in diabetes from self-monitoring data |
US6610012B2 (en) | 2000-04-10 | 2003-08-26 | Healthetech, Inc. | System and method for remote pregnancy monitoring |
US6440068B1 (en) | 2000-04-28 | 2002-08-27 | International Business Machines Corporation | Measuring user health as measured by multiple diverse health measurement devices utilizing a personal storage device |
WO2001088524A1 (en) | 2000-05-12 | 2001-11-22 | Therasense, Inc. | Electrodes with multilayer membranes and methods of using and making the electrodes |
US6442413B1 (en) | 2000-05-15 | 2002-08-27 | James H. Silver | Implantable sensor |
US7181261B2 (en) | 2000-05-15 | 2007-02-20 | Silver James H | Implantable, retrievable, thrombus minimizing sensors |
US7395158B2 (en) | 2000-05-30 | 2008-07-01 | Sensys Medical, Inc. | Method of screening for disorders of glucose metabolism |
US6361503B1 (en) | 2000-06-26 | 2002-03-26 | Mediwave Star Technology, Inc. | Method and system for evaluating cardiac ischemia |
WO2002015777A1 (en) | 2000-08-18 | 2002-02-28 | Cygnus, Inc. | Methods and devices for prediction of hypoglycemic events |
WO2002017210A2 (en) | 2000-08-18 | 2002-02-28 | Cygnus, Inc. | Formulation and manipulation of databases of analyte and associated values |
EP1311189A4 (en) | 2000-08-21 | 2005-03-09 | Euro Celtique Sa | Near infrared blood glucose monitoring system |
CA2426937A1 (en) | 2000-10-26 | 2002-05-02 | Medtronic, Inc. | Method and apparatus to minimize the effects of a cardiac insult |
US6695860B1 (en) * | 2000-11-13 | 2004-02-24 | Isense Corp. | Transcutaneous sensor insertion device |
US7052483B2 (en) | 2000-12-19 | 2006-05-30 | Animas Corporation | Transcutaneous inserter for low-profile infusion sets |
US6490479B2 (en) | 2000-12-28 | 2002-12-03 | Ge Medical Systems Information Technologies, Inc. | Atrial fibrillation detection method and apparatus |
US6560471B1 (en) | 2001-01-02 | 2003-05-06 | Therasense, Inc. | Analyte monitoring device and methods of use |
US6666821B2 (en) | 2001-01-08 | 2003-12-23 | Medtronic, Inc. | Sensor system |
US6970529B2 (en) * | 2001-01-16 | 2005-11-29 | International Business Machines Corporation | Unified digital architecture |
US20040197846A1 (en) | 2001-01-18 | 2004-10-07 | Linda Hockersmith | Determination of glucose sensitivity and a method to manipulate blood glucose concentration |
CA2435439A1 (en) | 2001-01-22 | 2002-07-25 | F. Hoffmann-La Roche Ag | Lancet device having capillary action |
AU2002248565A1 (en) | 2001-02-15 | 2002-08-28 | David A. Gough | Membrane and electrode structure for implantable sensor |
CA2440799A1 (en) | 2001-03-14 | 2002-09-19 | Baxter International Inc. | Internet based therapy management system |
US6968294B2 (en) | 2001-03-15 | 2005-11-22 | Koninklijke Philips Electronics N.V. | Automatic system for monitoring person requiring care and his/her caretaker |
US6622045B2 (en) | 2001-03-29 | 2003-09-16 | Pacesetter, Inc. | System and method for remote programming of implantable cardiac stimulation devices |
EP1397068A2 (en) | 2001-04-02 | 2004-03-17 | Therasense, Inc. | Blood glucose tracking apparatus and methods |
US6574490B2 (en) | 2001-04-11 | 2003-06-03 | Rio Grande Medical Technologies, Inc. | System for non-invasive measurement of glucose in humans |
US6698269B2 (en) | 2001-04-27 | 2004-03-02 | Oceana Sensor Technologies, Inc. | Transducer in-situ testing apparatus and method |
US7395214B2 (en) | 2001-05-11 | 2008-07-01 | Craig P Shillingburg | Apparatus, device and method for prescribing, administering and monitoring a treatment regimen for a patient |
US6676816B2 (en) | 2001-05-11 | 2004-01-13 | Therasense, Inc. | Transition metal complexes with (pyridyl)imidazole ligands and sensors using said complexes |
US6932894B2 (en) * | 2001-05-15 | 2005-08-23 | Therasense, Inc. | Biosensor membranes composed of polymers containing heterocyclic nitrogens |
US7041068B2 (en) | 2001-06-12 | 2006-05-09 | Pelikan Technologies, Inc. | Sampling module device and method |
US7179226B2 (en) | 2001-06-21 | 2007-02-20 | Animas Corporation | System and method for managing diabetes |
EP2465419B1 (en) | 2001-06-22 | 2015-08-05 | Nellcor Puritan Bennett Ireland | Wavelet-based analysis of pulse oximetry signals |
US7011630B2 (en) | 2001-06-22 | 2006-03-14 | Animas Technologies, Llc | Methods for computing rolling analyte measurement values, microprocessors comprising programming to control performance of the methods, and analyte monitoring devices employing the methods |
US7044911B2 (en) * | 2001-06-29 | 2006-05-16 | Philometron, Inc. | Gateway platform for biological monitoring and delivery of therapeutic compounds |
US6697658B2 (en) | 2001-07-02 | 2004-02-24 | Masimo Corporation | Low power pulse oximeter |
US20030208113A1 (en) | 2001-07-18 | 2003-11-06 | Mault James R | Closed loop glycemic index system |
US6754516B2 (en) | 2001-07-19 | 2004-06-22 | Nellcor Puritan Bennett Incorporated | Nuisance alarm reductions in a physiological monitor |
US20030032874A1 (en) * | 2001-07-27 | 2003-02-13 | Dexcom, Inc. | Sensor head for use with implantable devices |
US6702857B2 (en) * | 2001-07-27 | 2004-03-09 | Dexcom, Inc. | Membrane for use with implantable devices |
US6544212B2 (en) | 2001-07-31 | 2003-04-08 | Roche Diagnostics Corporation | Diabetes management system |
WO2003014735A1 (en) | 2001-08-03 | 2003-02-20 | General Hospital Corporation | System, process and diagnostic arrangement establishing and monitoring medication doses for patients |
US20040142403A1 (en) | 2001-08-13 | 2004-07-22 | Donald Hetzel | Method of screening for disorders of glucose metabolism |
EP1320322A1 (en) | 2001-08-20 | 2003-06-25 | Inverness Medical Limited | Wireless diabetes management devices and methods for using the same |
AU2002327513B2 (en) | 2001-08-22 | 2007-07-26 | Instrumentation Laboratory Company | Method and apparatus for calibrating electrochemical sensors |
IL160654A0 (en) | 2001-08-28 | 2004-07-25 | Medtronic Inc | Implantable medical device for treating cardiac mechanical dysfunction by electrical stimulation |
US6827702B2 (en) | 2001-09-07 | 2004-12-07 | Medtronic Minimed, Inc. | Safety limits for closed-loop infusion pump control |
JP2003084101A (en) | 2001-09-17 | 2003-03-19 | Dainippon Printing Co Ltd | Resin composition for optical device, optical device and projection screen |
US7052591B2 (en) | 2001-09-21 | 2006-05-30 | Therasense, Inc. | Electrodeposition of redox polymers and co-electrodeposition of enzymes by coordinative crosslinking |
US6830562B2 (en) | 2001-09-27 | 2004-12-14 | Unomedical A/S | Injector device for placing a subcutaneous infusion set |
US6731985B2 (en) | 2001-10-16 | 2004-05-04 | Pacesetter, Inc. | Implantable cardiac stimulation system and method for automatic capture verification calibration |
US7854230B2 (en) | 2001-10-22 | 2010-12-21 | O.R. Solutions, Inc. | Heated medical instrument stand with surgical drape and method of detecting fluid and leaks in the stand tray |
US7204823B2 (en) | 2001-12-19 | 2007-04-17 | Medtronic Minimed, Inc. | Medication delivery system and monitor |
US7399277B2 (en) * | 2001-12-27 | 2008-07-15 | Medtronic Minimed, Inc. | System for monitoring physiological characteristics |
US20080255438A1 (en) | 2001-12-27 | 2008-10-16 | Medtronic Minimed, Inc. | System for monitoring physiological characteristics |
US20050027182A1 (en) | 2001-12-27 | 2005-02-03 | Uzair Siddiqui | System for monitoring physiological characteristics |
US7022072B2 (en) | 2001-12-27 | 2006-04-04 | Medtronic Minimed, Inc. | System for monitoring physiological characteristics |
US7169107B2 (en) | 2002-01-25 | 2007-01-30 | Karen Jersey-Willuhn | Conductivity reconstruction based on inverse finite element measurements in a tissue monitoring system |
US8260393B2 (en) * | 2003-07-25 | 2012-09-04 | Dexcom, Inc. | Systems and methods for replacing signal data artifacts in a glucose sensor data stream |
US8364229B2 (en) | 2003-07-25 | 2013-01-29 | Dexcom, Inc. | Analyte sensors having a signal-to-noise ratio substantially unaffected by non-constant noise |
US9247901B2 (en) * | 2003-08-22 | 2016-02-02 | Dexcom, Inc. | Systems and methods for replacing signal artifacts in a glucose sensor data stream |
US8010174B2 (en) * | 2003-08-22 | 2011-08-30 | Dexcom, Inc. | Systems and methods for replacing signal artifacts in a glucose sensor data stream |
US10022078B2 (en) * | 2004-07-13 | 2018-07-17 | Dexcom, Inc. | Analyte sensor |
US7613491B2 (en) | 2002-05-22 | 2009-11-03 | Dexcom, Inc. | Silicone based membranes for use in implantable glucose sensors |
US20030212379A1 (en) | 2002-02-26 | 2003-11-13 | Bylund Adam David | Systems and methods for remotely controlling medication infusion and analyte monitoring |
EP1487519B1 (en) | 2002-02-26 | 2013-06-12 | TecPharma Licensing AG | Insertion device for an insertion set and method of using the same |
US6998247B2 (en) | 2002-03-08 | 2006-02-14 | Sensys Medical, Inc. | Method and apparatus using alternative site glucose determinations to calibrate and maintain noninvasive and implantable analyzers |
DE60337038D1 (en) | 2002-03-22 | 2011-06-16 | Animas Technologies Llc | Performance improvement of an analyte monitoring device |
US6936006B2 (en) | 2002-03-22 | 2005-08-30 | Novo Nordisk, A/S | Atraumatic insertion of a subcutaneous device |
GB2388898B (en) | 2002-04-02 | 2005-10-05 | Inverness Medical Ltd | Integrated sample testing meter |
US7027848B2 (en) | 2002-04-04 | 2006-04-11 | Inlight Solutions, Inc. | Apparatus and method for non-invasive spectroscopic measurement of analytes in tissue using a matched reference analyte |
US7410468B2 (en) | 2002-04-19 | 2008-08-12 | Pelikan Technologies, Inc. | Method and apparatus for penetrating tissue |
US7226461B2 (en) | 2002-04-19 | 2007-06-05 | Pelikan Technologies, Inc. | Method and apparatus for a multi-use body fluid sampling device with sterility barrier release |
US7153265B2 (en) | 2002-04-22 | 2006-12-26 | Medtronic Minimed, Inc. | Anti-inflammatory biosensor for reduced biofouling and enhanced sensor performance |
US20050177398A1 (en) | 2002-04-25 | 2005-08-11 | Motokazu Watanabe | Dosage determination supporting device, injector, and health management supporting system |
US7226978B2 (en) | 2002-05-22 | 2007-06-05 | Dexcom, Inc. | Techniques to improve polyurethane membranes for implantable glucose sensors |
US6865407B2 (en) | 2002-07-11 | 2005-03-08 | Optical Sensors, Inc. | Calibration technique for non-invasive medical devices |
US20040010207A1 (en) * | 2002-07-15 | 2004-01-15 | Flaherty J. Christopher | Self-contained, automatic transcutaneous physiologic sensing system |
US7034677B2 (en) | 2002-07-19 | 2006-04-25 | Smiths Detection Inc. | Non-specific sensor array detectors |
US7278983B2 (en) | 2002-07-24 | 2007-10-09 | Medtronic Minimed, Inc. | Physiological monitoring device for controlling a medication infusion device |
EP2322092B1 (en) | 2002-08-13 | 2015-11-25 | University Of Virginia Patent Foundation | Method, system, and computer program product for processing of self-monitoring blood glucose (smbg) data to enhance diabetic self-management |
US7404796B2 (en) | 2004-03-01 | 2008-07-29 | Becton Dickinson And Company | System for determining insulin dose using carbohydrate to insulin ratio and insulin sensitivity factor |
US6912413B2 (en) | 2002-09-13 | 2005-06-28 | Ge Healthcare Finland Oy | Pulse oximeter |
US7192405B2 (en) | 2002-09-30 | 2007-03-20 | Becton, Dickinson And Company | Integrated lancet and bodily fluid sensor |
ATE433775T1 (en) | 2002-10-11 | 2009-07-15 | Becton Dickinson Co | INSULIN DELIVERY SYSTEM WITH SENSOR |
US7029443B2 (en) | 2002-10-21 | 2006-04-18 | Pacesetter, Inc. | System and method for monitoring blood glucose levels using an implantable medical device |
US7381184B2 (en) | 2002-11-05 | 2008-06-03 | Abbott Diabetes Care Inc. | Sensor inserter assembly |
US7572237B2 (en) | 2002-11-06 | 2009-08-11 | Abbott Diabetes Care Inc. | Automatic biological analyte testing meter with integrated lancing device and methods of use |
US6931328B2 (en) | 2002-11-08 | 2005-08-16 | Optiscan Biomedical Corp. | Analyte detection system with software download capabilities |
US7052472B1 (en) | 2002-12-18 | 2006-05-30 | Dsp Diabetes Sentry Products, Inc. | Systems and methods for detecting symptoms of hypoglycemia |
US20040122353A1 (en) | 2002-12-19 | 2004-06-24 | Medtronic Minimed, Inc. | Relay device for transferring information between a sensor system and a fluid delivery system |
AU2003303597A1 (en) | 2002-12-31 | 2004-07-29 | Therasense, Inc. | Continuous glucose monitoring system and methods of use |
US7396330B2 (en) * | 2003-01-07 | 2008-07-08 | Triage Data Networks | Wireless, internet-based medical-diagnostic system |
US7207947B2 (en) | 2003-01-10 | 2007-04-24 | Pacesetter, Inc. | System and method for detecting circadian states using an implantable medical device |
US20040172307A1 (en) | 2003-02-06 | 2004-09-02 | Gruber Martin A. | Electronic medical record method |
WO2004084820A2 (en) | 2003-03-19 | 2004-10-07 | Harry Hebblewhite | Method and system for determining insulin dosing schedules and carbohydrate-to-insulin ratios in diabetic patients |
US7134999B2 (en) | 2003-04-04 | 2006-11-14 | Dexcom, Inc. | Optimized sensor geometry for an implantable glucose sensor |
AU2004232289A1 (en) | 2003-04-18 | 2004-11-04 | Insulet Corporation | User interface for infusion pump remote controller and method of using the same |
US7103412B1 (en) | 2003-05-02 | 2006-09-05 | Pacesetter, Inc. | Implantable cardiac stimulation device and method for detecting asymptomatic diabetes |
US7875293B2 (en) * | 2003-05-21 | 2011-01-25 | Dexcom, Inc. | Biointerface membranes incorporating bioactive agents |
US20050016276A1 (en) * | 2003-06-06 | 2005-01-27 | Palo Alto Sensor Technology Innovation | Frequency encoding of resonant mass sensors |
US7258673B2 (en) | 2003-06-06 | 2007-08-21 | Lifescan, Inc | Devices, systems and methods for extracting bodily fluid and monitoring an analyte therein |
US8460243B2 (en) | 2003-06-10 | 2013-06-11 | Abbott Diabetes Care Inc. | Glucose measuring module and insulin pump combination |
US8066639B2 (en) * | 2003-06-10 | 2011-11-29 | Abbott Diabetes Care Inc. | Glucose measuring device for use in personal area network |
US20040254433A1 (en) | 2003-06-12 | 2004-12-16 | Bandis Steven D. | Sensor introducer system, apparatus and method |
US7142911B2 (en) | 2003-06-26 | 2006-11-28 | Pacesetter, Inc. | Method and apparatus for monitoring drug effects on cardiac electrical signals using an implantable cardiac stimulation device |
US7510564B2 (en) | 2003-06-27 | 2009-03-31 | Abbott Diabetes Care Inc. | Lancing device |
WO2005007223A2 (en) | 2003-07-16 | 2005-01-27 | Sasha John | Programmable medical drug delivery systems and methods for delivery of multiple fluids and concentrations |
US7467003B2 (en) | 2003-12-05 | 2008-12-16 | Dexcom, Inc. | Dual electrode system for a continuous analyte sensor |
WO2005019795A2 (en) | 2003-07-25 | 2005-03-03 | Dexcom, Inc. | Electrochemical sensors including electrode systems with increased oxygen generation |
EP1648298A4 (en) | 2003-07-25 | 2010-01-13 | Dexcom Inc | Oxygen enhancing membrane systems for implantable devices |
US20050176136A1 (en) | 2003-11-19 | 2005-08-11 | Dexcom, Inc. | Afinity domain for analyte sensor |
US7424318B2 (en) | 2003-12-05 | 2008-09-09 | Dexcom, Inc. | Dual electrode system for a continuous analyte sensor |
JP2007500336A (en) | 2003-07-25 | 2007-01-11 | デックスコム・インコーポレーテッド | Electrode system for electrochemical sensors |
US8282549B2 (en) * | 2003-12-09 | 2012-10-09 | Dexcom, Inc. | Signal processing for continuous analyte sensor |
WO2007120442A2 (en) | 2003-07-25 | 2007-10-25 | Dexcom, Inc. | Dual electrode system for a continuous analyte sensor |
US7460898B2 (en) | 2003-12-05 | 2008-12-02 | Dexcom, Inc. | Dual electrode system for a continuous analyte sensor |
US8423113B2 (en) | 2003-07-25 | 2013-04-16 | Dexcom, Inc. | Systems and methods for processing sensor data |
US7366556B2 (en) | 2003-12-05 | 2008-04-29 | Dexcom, Inc. | Dual electrode system for a continuous analyte sensor |
JP4771947B2 (en) | 2003-07-25 | 2011-09-14 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | System and method for monitoring a system |
US8275437B2 (en) | 2003-08-01 | 2012-09-25 | Dexcom, Inc. | Transcutaneous analyte sensor |
US7494465B2 (en) * | 2004-07-13 | 2009-02-24 | Dexcom, Inc. | Transcutaneous analyte sensor |
US7774145B2 (en) | 2003-08-01 | 2010-08-10 | Dexcom, Inc. | Transcutaneous analyte sensor |
US9135402B2 (en) | 2007-12-17 | 2015-09-15 | Dexcom, Inc. | Systems and methods for processing sensor data |
US7519408B2 (en) | 2003-11-19 | 2009-04-14 | Dexcom, Inc. | Integrated receiver for continuous analyte sensor |
US8626257B2 (en) | 2003-08-01 | 2014-01-07 | Dexcom, Inc. | Analyte sensor |
US7276029B2 (en) | 2003-08-01 | 2007-10-02 | Dexcom, Inc. | System and methods for processing analyte sensor data |
US8886273B2 (en) | 2003-08-01 | 2014-11-11 | Dexcom, Inc. | Analyte sensor |
US7933639B2 (en) | 2003-08-01 | 2011-04-26 | Dexcom, Inc. | System and methods for processing analyte sensor data |
US8369919B2 (en) | 2003-08-01 | 2013-02-05 | Dexcom, Inc. | Systems and methods for processing sensor data |
US7591801B2 (en) | 2004-02-26 | 2009-09-22 | Dexcom, Inc. | Integrated delivery device for continuous glucose sensor |
US8761856B2 (en) | 2003-08-01 | 2014-06-24 | Dexcom, Inc. | System and methods for processing analyte sensor data |
US6954662B2 (en) | 2003-08-19 | 2005-10-11 | A.D. Integrity Applications, Ltd. | Method of monitoring glucose level |
US7920906B2 (en) | 2005-03-10 | 2011-04-05 | Dexcom, Inc. | System and methods for processing analyte sensor data for sensor calibration |
DE602004028143D1 (en) | 2003-10-13 | 2010-08-26 | Novo Nordisk As | DEVICE AND METHOD FOR DETERMINING A PHYSIOLOGICAL CONDITION |
US20050090607A1 (en) | 2003-10-28 | 2005-04-28 | Dexcom, Inc. | Silicone composition for biocompatible membrane |
US6928380B2 (en) | 2003-10-30 | 2005-08-09 | International Business Machines Corporation | Thermal measurements of electronic devices during operation |
US7299082B2 (en) | 2003-10-31 | 2007-11-20 | Abbott Diabetes Care, Inc. | Method of calibrating an analyte-measurement device, and associated methods, devices and systems |
WO2005044088A2 (en) | 2003-11-03 | 2005-05-19 | Children's Medical Center Corporation | Continuous analyte monitor and method of using same |
EP2239567B1 (en) | 2003-12-05 | 2015-09-02 | DexCom, Inc. | Calibration techniques for a continuous analyte sensor |
US8364231B2 (en) * | 2006-10-04 | 2013-01-29 | Dexcom, Inc. | Analyte sensor |
US8423114B2 (en) | 2006-10-04 | 2013-04-16 | Dexcom, Inc. | Dual electrode system for a continuous analyte sensor |
US20080197024A1 (en) | 2003-12-05 | 2008-08-21 | Dexcom, Inc. | Analyte sensor |
US20080200788A1 (en) | 2006-10-04 | 2008-08-21 | Dexcorn, Inc. | Analyte sensor |
US8425416B2 (en) | 2006-10-04 | 2013-04-23 | Dexcom, Inc. | Analyte sensor |
US8287453B2 (en) | 2003-12-05 | 2012-10-16 | Dexcom, Inc. | Analyte sensor |
US8774886B2 (en) | 2006-10-04 | 2014-07-08 | Dexcom, Inc. | Analyte sensor |
US8425417B2 (en) | 2003-12-05 | 2013-04-23 | Dexcom, Inc. | Integrated device for continuous in vivo analyte detection and simultaneous control of an infusion device |
US8364230B2 (en) | 2006-10-04 | 2013-01-29 | Dexcom, Inc. | Analyte sensor |
DE602004028164D1 (en) | 2003-12-08 | 2010-08-26 | Dexcom Inc | SYSTEMS AND METHOD FOR IMPROVING ELECTROCHEMICAL ANALYTIC SENSORS |
US7076300B1 (en) | 2003-12-24 | 2006-07-11 | Pacesetter, Inc. | Implantable cardiac stimulation device and method that discriminates between and treats atrial tachycardia and atrial fibrillation |
US7637868B2 (en) | 2004-01-12 | 2009-12-29 | Dexcom, Inc. | Composite material for implantable device |
US7580812B2 (en) | 2004-01-28 | 2009-08-25 | Honeywell International Inc. | Trending system and method using window filtering |
US8165651B2 (en) | 2004-02-09 | 2012-04-24 | Abbott Diabetes Care Inc. | Analyte sensor, and associated system and method employing a catalytic agent |
US7699964B2 (en) | 2004-02-09 | 2010-04-20 | Abbott Diabetes Care Inc. | Membrane suitable for use in an analyte sensor, analyte sensor, and associated method |
US7364592B2 (en) | 2004-02-12 | 2008-04-29 | Dexcom, Inc. | Biointerface membrane with macro-and micro-architecture |
EP1718198A4 (en) | 2004-02-17 | 2008-06-04 | Therasense Inc | Method and system for providing data communication in continuous glucose monitoring and management system |
CA2556592C (en) | 2004-02-26 | 2014-01-28 | Lars Gustaf Liljeryd | Metabolic monitoring, a method and apparatus for indicating a health-related condition of a subject |
US8808228B2 (en) | 2004-02-26 | 2014-08-19 | Dexcom, Inc. | Integrated medicament delivery device for use with continuous analyte sensor |
DE102004011135A1 (en) | 2004-03-08 | 2005-09-29 | Disetronic Licensing Ag | Method and apparatus for calculating a bolus amount |
DK1734858T3 (en) | 2004-03-22 | 2014-10-20 | Bodymedia Inc | NON-INVASIVE TEMPERATURE MONITORING DEVICE |
JP2007535974A (en) | 2004-03-26 | 2007-12-13 | ノボ・ノルデイスク・エー/エス | Display device for related data of diabetic patients |
US6971274B2 (en) | 2004-04-02 | 2005-12-06 | Sierra Instruments, Inc. | Immersible thermal mass flow meter |
WO2005106017A2 (en) | 2004-04-21 | 2005-11-10 | University Of Virginia Patent Foundation | Method, system and computer program product for evaluating the accuracy of blood glucose monitoring sensors/devices |
US20050245799A1 (en) | 2004-05-03 | 2005-11-03 | Dexcom, Inc. | Implantable analyte sensor |
US8277713B2 (en) * | 2004-05-03 | 2012-10-02 | Dexcom, Inc. | Implantable analyte sensor |
US7118667B2 (en) | 2004-06-02 | 2006-10-10 | Jin Po Lee | Biosensors having improved sample application and uses thereof |
CA2572455C (en) | 2004-06-04 | 2014-10-28 | Therasense, Inc. | Diabetes care host-client architecture and data management system |
US7623988B2 (en) * | 2004-06-23 | 2009-11-24 | Cybiocare Inc. | Method and apparatus for the monitoring of clinical states |
US7233822B2 (en) | 2004-06-29 | 2007-06-19 | Medtronic, Inc. | Combination of electrogram and intra-cardiac pressure to discriminate between fibrillation and tachycardia |
US20060001538A1 (en) * | 2004-06-30 | 2006-01-05 | Ulrich Kraft | Methods of monitoring the concentration of an analyte |
US20060015020A1 (en) * | 2004-07-06 | 2006-01-19 | Dexcom, Inc. | Systems and methods for manufacture of an analyte-measuring device including a membrane system |
US7640048B2 (en) | 2004-07-13 | 2009-12-29 | Dexcom, Inc. | Analyte sensor |
US7783333B2 (en) | 2004-07-13 | 2010-08-24 | Dexcom, Inc. | Transcutaneous medical device with variable stiffness |
US8452368B2 (en) | 2004-07-13 | 2013-05-28 | Dexcom, Inc. | Transcutaneous analyte sensor |
US20060020192A1 (en) * | 2004-07-13 | 2006-01-26 | Dexcom, Inc. | Transcutaneous analyte sensor |
US20080242961A1 (en) | 2004-07-13 | 2008-10-02 | Dexcom, Inc. | Transcutaneous analyte sensor |
US8565848B2 (en) | 2004-07-13 | 2013-10-22 | Dexcom, Inc. | Transcutaneous analyte sensor |
US7344500B2 (en) | 2004-07-27 | 2008-03-18 | Medtronic Minimed, Inc. | Sensing system with auxiliary display |
US8313433B2 (en) * | 2004-08-06 | 2012-11-20 | Medtronic Minimed, Inc. | Medical data management system and process |
JP2008511373A (en) | 2004-09-03 | 2008-04-17 | ノボ・ノルデイスク・エー/エス | Method for calibrating a system for measuring the concentration of a body substance and apparatus for carrying out the method |
US7468033B2 (en) | 2004-09-08 | 2008-12-23 | Medtronic Minimed, Inc. | Blood contacting sensor |
JPWO2006070827A1 (en) | 2004-12-28 | 2008-06-12 | 新世代株式会社 | Health care support system and recording medium |
US7883464B2 (en) | 2005-09-30 | 2011-02-08 | Abbott Diabetes Care Inc. | Integrated transmitter unit and sensor introducer mechanism and methods of use |
US8512243B2 (en) | 2005-09-30 | 2013-08-20 | Abbott Diabetes Care Inc. | Integrated introducer and transmitter assembly and methods of use |
US20070027381A1 (en) | 2005-07-29 | 2007-02-01 | Therasense, Inc. | Inserter and methods of use |
US7731657B2 (en) | 2005-08-30 | 2010-06-08 | Abbott Diabetes Care Inc. | Analyte sensor introducer and methods of use |
US20090082693A1 (en) | 2004-12-29 | 2009-03-26 | Therasense, Inc. | Method and apparatus for providing temperature sensor module in a data communication system |
US9398882B2 (en) * | 2005-09-30 | 2016-07-26 | Abbott Diabetes Care Inc. | Method and apparatus for providing analyte sensor and data processing device |
US20060166629A1 (en) | 2005-01-24 | 2006-07-27 | Therasense, Inc. | Method and apparatus for providing EMC Class-B compliant RF transmitter for data monitoring an detection systems |
US7297114B2 (en) | 2005-01-25 | 2007-11-20 | Pacesetter, Inc. | System and method for distinguishing among cardiac ischemia, hypoglycemia and hyperglycemia using an implantable medical device |
US20060173260A1 (en) | 2005-01-31 | 2006-08-03 | Gmms Ltd | System, device and method for diabetes treatment and monitoring |
US7547281B2 (en) | 2005-02-01 | 2009-06-16 | Medtronic Minimed, Inc. | Algorithm sensor augmented bolus estimator for semi-closed loop infusion system |
US7499002B2 (en) | 2005-02-08 | 2009-03-03 | International Business Machines Corporation | Retractable string interface for stationary and portable devices |
US7545272B2 (en) | 2005-02-08 | 2009-06-09 | Therasense, Inc. | RF tag on test strips, test strip vials and boxes |
AU2006212007A1 (en) | 2005-02-11 | 2006-08-17 | The University Court Of The University Of Glasgow | Sensing device, apparatus and system, and method for operating the same |
KR100638727B1 (en) | 2005-02-28 | 2006-10-30 | 삼성전기주식회사 | Concurrent transceiver for zigbee and bluetooth |
US20090076360A1 (en) | 2007-09-13 | 2009-03-19 | Dexcom, Inc. | Transcutaneous analyte sensor |
JP2008538024A (en) | 2005-03-15 | 2008-10-02 | エンテロス・インコーポレーテッド | Apparatus and method for computer modeling of type 1 diabetes |
US7889069B2 (en) | 2005-04-01 | 2011-02-15 | Codman & Shurtleff, Inc. | Wireless patient monitoring system |
WO2006110193A2 (en) | 2005-04-08 | 2006-10-19 | Dexcom, Inc. | Cellulosic-based interference domain for an analyte sensor |
US20090054753A1 (en) | 2007-08-21 | 2009-02-26 | Mark Ries Robinson | Variable Sampling Interval for Blood Analyte Determinations |
US7590443B2 (en) | 2005-04-27 | 2009-09-15 | Pacesetter, Inc | System and method for detecting hypoglycemia based on a paced depolarization integral using an implantable medical device |
US20060247985A1 (en) | 2005-04-29 | 2006-11-02 | Therasense, Inc. | Method and system for monitoring consumable item usage and providing replenishment thereof |
US8112240B2 (en) | 2005-04-29 | 2012-02-07 | Abbott Diabetes Care Inc. | Method and apparatus for providing leak detection in data monitoring and management systems |
KR101381331B1 (en) | 2005-05-09 | 2014-04-04 | 테라노스, 인코포레이티드 | Point-of-care fluidic systems and uses thereof |
US7604178B2 (en) | 2005-05-11 | 2009-10-20 | Intelleflex Corporation | Smart tag activation |
JP5037496B2 (en) | 2005-05-13 | 2012-09-26 | トラスティーズ オブ ボストン ユニバーシティ | Fully automatic control system for type 1 diabetes |
US7541935B2 (en) | 2005-05-19 | 2009-06-02 | Proacticare Llc | System and methods for monitoring caregiver performance |
US7976466B2 (en) | 2005-06-02 | 2011-07-12 | Isense Corporation | Use of multiple data points and filtering in an analyte sensor |
US20060272652A1 (en) | 2005-06-03 | 2006-12-07 | Medtronic Minimed, Inc. | Virtual patient software system for educating and treating individuals with diabetes |
US20070033074A1 (en) * | 2005-06-03 | 2007-02-08 | Medtronic Minimed, Inc. | Therapy management system |
EP1741384B1 (en) | 2005-07-08 | 2009-11-11 | Draeger Medical Systems, Inc. | A system for adjusting power employed by a medical device |
CN102440785A (en) | 2005-08-31 | 2012-05-09 | 弗吉尼亚大学专利基金委员会 | Sensor signal processing method and sensor signal processing device |
WO2007028271A2 (en) | 2005-09-09 | 2007-03-15 | F. Hoffmann-La Roche Ag | A system, tools, devices and a program for diabetes care |
US8298389B2 (en) | 2005-09-12 | 2012-10-30 | Abbott Diabetes Care Inc. | In vitro analyte sensor, and methods |
US9072476B2 (en) | 2005-09-23 | 2015-07-07 | Medtronic Minimed, Inc. | Flexible sensor apparatus |
US7846311B2 (en) | 2005-09-27 | 2010-12-07 | Abbott Diabetes Care Inc. | In vitro analyte sensor and methods of use |
US7756561B2 (en) | 2005-09-30 | 2010-07-13 | Abbott Diabetes Care Inc. | Method and apparatus for providing rechargeable power in data monitoring and management systems |
US9521968B2 (en) | 2005-09-30 | 2016-12-20 | Abbott Diabetes Care Inc. | Analyte sensor retention mechanism and methods of use |
US7468125B2 (en) | 2005-10-17 | 2008-12-23 | Lifescan, Inc. | System and method of processing a current sample for calculating a glucose concentration |
US20070095661A1 (en) | 2005-10-31 | 2007-05-03 | Yi Wang | Method of making, and, analyte sensor |
US7766829B2 (en) | 2005-11-04 | 2010-08-03 | Abbott Diabetes Care Inc. | Method and system for providing basal profile modification in analyte monitoring and management systems |
WO2007056592A2 (en) | 2005-11-08 | 2007-05-18 | M2 Medical A/S | Method and system for manual and autonomous control of an infusion pump |
US20070173706A1 (en) | 2005-11-11 | 2007-07-26 | Isense Corporation | Method and apparatus for insertion of a sensor |
US7918975B2 (en) | 2005-11-17 | 2011-04-05 | Abbott Diabetes Care Inc. | Analytical sensors for biological fluid |
US20070118030A1 (en) | 2005-11-22 | 2007-05-24 | Isense Corporation | Method and apparatus for analyte data telemetry |
WO2007062173A1 (en) | 2005-11-22 | 2007-05-31 | Vocollect Healthcare Systems, Inc. | Advanced diabetes management system (adms) |
US7963917B2 (en) | 2005-12-05 | 2011-06-21 | Echo Therapeutics, Inc. | System and method for continuous non-invasive glucose monitoring |
US7941200B2 (en) | 2005-12-08 | 2011-05-10 | Roche Diagnostics Operations, Inc. | System and method for determining drug administration information |
CA2636034A1 (en) | 2005-12-28 | 2007-10-25 | Abbott Diabetes Care Inc. | Medical device insertion |
US8160670B2 (en) | 2005-12-28 | 2012-04-17 | Abbott Diabetes Care Inc. | Analyte monitoring: stabilizer for subcutaneous glucose sensor with incorporated antiglycolytic agent |
US8515518B2 (en) | 2005-12-28 | 2013-08-20 | Abbott Diabetes Care Inc. | Analyte monitoring |
EP2004796B1 (en) | 2006-01-18 | 2015-04-08 | DexCom, Inc. | Membranes for an analyte sensor |
US20070179349A1 (en) | 2006-01-19 | 2007-08-02 | Hoyme Kenneth P | System and method for providing goal-oriented patient management based upon comparative population data analysis |
US7736310B2 (en) | 2006-01-30 | 2010-06-15 | Abbott Diabetes Care Inc. | On-body medical device securement |
WO2007097754A1 (en) | 2006-02-22 | 2007-08-30 | Dexcom, Inc. | Analyte sensor |
JP5234967B2 (en) | 2006-02-27 | 2013-07-10 | エドワーズ ライフサイエンシーズ コーポレイション | Flux limiting membranes for intravenous amperometric biosensors |
US7826879B2 (en) | 2006-02-28 | 2010-11-02 | Abbott Diabetes Care Inc. | Analyte sensors and methods of use |
US7981034B2 (en) | 2006-02-28 | 2011-07-19 | Abbott Diabetes Care Inc. | Smart messages and alerts for an infusion delivery and management system |
US7885698B2 (en) | 2006-02-28 | 2011-02-08 | Abbott Diabetes Care Inc. | Method and system for providing continuous calibration of implantable analyte sensors |
US7811430B2 (en) | 2006-02-28 | 2010-10-12 | Abbott Diabetes Care Inc. | Biosensors and methods of making |
EP1991110B1 (en) | 2006-03-09 | 2018-11-07 | DexCom, Inc. | Systems and methods for processing analyte sensor data |
US7887682B2 (en) | 2006-03-29 | 2011-02-15 | Abbott Diabetes Care Inc. | Analyte sensors and methods of use |
US9339217B2 (en) | 2011-11-25 | 2016-05-17 | Abbott Diabetes Care Inc. | Analyte monitoring system and methods of use |
US9392969B2 (en) | 2008-08-31 | 2016-07-19 | Abbott Diabetes Care Inc. | Closed loop control and signal attenuation detection |
US7630748B2 (en) | 2006-10-25 | 2009-12-08 | Abbott Diabetes Care Inc. | Method and system for providing analyte monitoring |
US8140312B2 (en) | 2007-05-14 | 2012-03-20 | Abbott Diabetes Care Inc. | Method and system for determining analyte levels |
US20070233013A1 (en) | 2006-03-31 | 2007-10-04 | Schoenberg Stephen J | Covers for tissue engaging members |
US7620438B2 (en) | 2006-03-31 | 2009-11-17 | Abbott Diabetes Care Inc. | Method and system for powering an electronic device |
US8473022B2 (en) | 2008-01-31 | 2013-06-25 | Abbott Diabetes Care Inc. | Analyte sensor with time lag compensation |
US8346335B2 (en) | 2008-03-28 | 2013-01-01 | Abbott Diabetes Care Inc. | Analyte sensor calibration management |
US7618369B2 (en) | 2006-10-02 | 2009-11-17 | Abbott Diabetes Care Inc. | Method and system for dynamically updating calibration parameters for an analyte sensor |
US8226891B2 (en) | 2006-03-31 | 2012-07-24 | Abbott Diabetes Care Inc. | Analyte monitoring devices and methods therefor |
US7653425B2 (en) * | 2006-08-09 | 2010-01-26 | Abbott Diabetes Care Inc. | Method and system for providing calibration of an analyte sensor in an analyte monitoring system |
US8224415B2 (en) | 2009-01-29 | 2012-07-17 | Abbott Diabetes Care Inc. | Method and device for providing offset model based calibration for analyte sensor |
US8380300B2 (en) | 2006-04-28 | 2013-02-19 | Medtronic, Inc. | Efficacy visualization |
US20090054749A1 (en) | 2006-05-31 | 2009-02-26 | Abbott Diabetes Care, Inc. | Method and System for Providing Data Transmission in a Data Management System |
US7920907B2 (en) | 2006-06-07 | 2011-04-05 | Abbott Diabetes Care Inc. | Analyte monitoring system and method |
US20080177149A1 (en) | 2006-06-16 | 2008-07-24 | Stefan Weinert | System and method for collecting patient information from which diabetes therapy may be determined |
US20070299617A1 (en) | 2006-06-27 | 2007-12-27 | Willis John P | Biofouling self-compensating biosensor |
US20080004601A1 (en) | 2006-06-28 | 2008-01-03 | Abbott Diabetes Care, Inc. | Analyte Monitoring and Therapy Management System and Methods Therefor |
US20090105560A1 (en) | 2006-06-28 | 2009-04-23 | David Solomon | Lifestyle and eating advisor based on physiological and biological rhythm monitoring |
US9119582B2 (en) | 2006-06-30 | 2015-09-01 | Abbott Diabetes Care, Inc. | Integrated analyte sensor and infusion device and methods therefor |
ES2802600T3 (en) | 2006-07-07 | 2021-01-20 | Hoffmann La Roche | Fluid Management Device and Operating Procedures |
CN101489469B (en) | 2006-07-10 | 2012-12-12 | 埃森哲环球服务有限公司 | Mobile personal services platform for providing feedback |
US7908334B2 (en) | 2006-07-21 | 2011-03-15 | Cardiac Pacemakers, Inc. | System and method for addressing implantable devices |
US7866026B1 (en) | 2006-08-01 | 2011-01-11 | Abbott Diabetes Care Inc. | Method for making calibration-adjusted sensors |
US8932216B2 (en) | 2006-08-07 | 2015-01-13 | Abbott Diabetes Care Inc. | Method and system for providing data management in integrated analyte monitoring and infusion system |
US9056165B2 (en) | 2006-09-06 | 2015-06-16 | Medtronic Minimed, Inc. | Intelligent therapy recommendation algorithm and method of using the same |
US8298142B2 (en) | 2006-10-04 | 2012-10-30 | Dexcom, Inc. | Analyte sensor |
US8447376B2 (en) | 2006-10-04 | 2013-05-21 | Dexcom, Inc. | Analyte sensor |
US8562528B2 (en) | 2006-10-04 | 2013-10-22 | Dexcom, Inc. | Analyte sensor |
US8275438B2 (en) | 2006-10-04 | 2012-09-25 | Dexcom, Inc. | Analyte sensor |
US8478377B2 (en) | 2006-10-04 | 2013-07-02 | Dexcom, Inc. | Analyte sensor |
US7831287B2 (en) | 2006-10-04 | 2010-11-09 | Dexcom, Inc. | Dual electrode system for a continuous analyte sensor |
US8449464B2 (en) | 2006-10-04 | 2013-05-28 | Dexcom, Inc. | Analyte sensor |
US8255026B1 (en) | 2006-10-12 | 2012-08-28 | Masimo Corporation, Inc. | Patient monitor capable of monitoring the quality of attached probes and accessories |
US8135548B2 (en) | 2006-10-26 | 2012-03-13 | Abbott Diabetes Care Inc. | Method, system and computer program product for real-time detection of sensitivity decline in analyte sensors |
US20130324823A1 (en) | 2006-11-20 | 2013-12-05 | Modz Oy | Measurement device, system and method |
US20080139910A1 (en) | 2006-12-06 | 2008-06-12 | Metronic Minimed, Inc. | Analyte sensor and method of using the same |
US20080154513A1 (en) | 2006-12-21 | 2008-06-26 | University Of Virginia Patent Foundation | Systems, Methods and Computer Program Codes for Recognition of Patterns of Hyperglycemia and Hypoglycemia, Increased Glucose Variability, and Ineffective Self-Monitoring in Diabetes |
US20080161666A1 (en) | 2006-12-29 | 2008-07-03 | Abbott Diabetes Care, Inc. | Analyte devices and methods |
US7946985B2 (en) | 2006-12-29 | 2011-05-24 | Medtronic Minimed, Inc. | Method and system for providing sensor redundancy |
US7734323B2 (en) | 2007-01-24 | 2010-06-08 | Smiths Medical Asd, Inc. | Correction factor testing using frequent blood glucose input |
US10154804B2 (en) | 2007-01-31 | 2018-12-18 | Medtronic Minimed, Inc. | Model predictive method and system for controlling and supervising insulin infusion |
US9597019B2 (en) | 2007-02-09 | 2017-03-21 | Lifescan, Inc. | Method of ensuring date and time on a test meter is accurate |
US8732188B2 (en) | 2007-02-18 | 2014-05-20 | Abbott Diabetes Care Inc. | Method and system for providing contextual based medication dosage determination |
US7751864B2 (en) | 2007-03-01 | 2010-07-06 | Roche Diagnostics Operations, Inc. | System and method for operating an electrochemical analyte sensor |
US20080234943A1 (en) | 2007-03-20 | 2008-09-25 | Pinaki Ray | Computer program for diabetes management |
US9204827B2 (en) | 2007-04-14 | 2015-12-08 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in medical communication system |
ES2784736T3 (en) | 2007-04-14 | 2020-09-30 | Abbott Diabetes Care Inc | Procedure and apparatus for providing data processing and control in a medical communication system |
EP2146627B1 (en) | 2007-04-14 | 2020-07-29 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in medical communication system |
EP2137637A4 (en) | 2007-04-14 | 2012-06-20 | Abbott Diabetes Care Inc | Method and apparatus for providing data processing and control in medical communication system |
EP2146625B1 (en) | 2007-04-14 | 2019-08-14 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in medical communication system |
US8236166B2 (en) | 2007-04-27 | 2012-08-07 | Abbott Diabetes Care Inc. | No calibration analyte sensors and methods |
US20080278332A1 (en) | 2007-05-08 | 2008-11-13 | Abbott Diabetes Care, Inc. | Analyte monitoring system and methods |
US10002233B2 (en) * | 2007-05-14 | 2018-06-19 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8444560B2 (en) | 2007-05-14 | 2013-05-21 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8260558B2 (en) | 2007-05-14 | 2012-09-04 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8103471B2 (en) | 2007-05-14 | 2012-01-24 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US9125548B2 (en) | 2007-05-14 | 2015-09-08 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8560038B2 (en) | 2007-05-14 | 2013-10-15 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8239166B2 (en) | 2007-05-14 | 2012-08-07 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US7996158B2 (en) | 2007-05-14 | 2011-08-09 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US20080312845A1 (en) | 2007-05-14 | 2008-12-18 | Abbott Diabetes Care, Inc. | Method and apparatus for providing data processing and control in a medical communication system |
EP2156684A4 (en) | 2007-05-14 | 2012-10-24 | Abbott Diabetes Care Inc | Method and apparatus for providing data processing and control in a medical communication system |
US8600681B2 (en) | 2007-05-14 | 2013-12-03 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US20080300572A1 (en) | 2007-06-01 | 2008-12-04 | Medtronic Minimed, Inc. | Wireless monitor for a personal medical device system |
EP2152350A4 (en) | 2007-06-08 | 2013-03-27 | Dexcom Inc | Integrated medicament delivery device for use with continuous analyte sensor |
EP2166929B1 (en) | 2007-06-15 | 2012-12-19 | F. Hoffmann-La Roche AG | Visualization of a parameter which is measured on the human body |
JP5680960B2 (en) | 2007-06-21 | 2015-03-04 | アボット ダイアベティス ケア インコーポレイテッドAbbott Diabetes Care Inc. | Health care device and method |
CN103251414B (en) | 2007-06-21 | 2017-05-24 | 雅培糖尿病护理公司 | Device for detecting analyte level |
US9754078B2 (en) | 2007-06-21 | 2017-09-05 | Immersion Corporation | Haptic health feedback monitoring |
KR101423807B1 (en) | 2007-06-27 | 2014-07-30 | 에프. 호프만-라 로슈 아게 | System and method for developing patient specific therapies based on modeling of patient physiology |
CA2687587C (en) | 2007-06-27 | 2018-08-28 | F. Hoffmann-La Roche Ag | Patient information input interface for a therapy system |
EP2170430A2 (en) | 2007-06-29 | 2010-04-07 | Roche Diagnostics GmbH | Method and apparatus for determining and delivering a drug bolus |
US20090036760A1 (en) * | 2007-07-31 | 2009-02-05 | Abbott Diabetes Care, Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US8834366B2 (en) | 2007-07-31 | 2014-09-16 | Abbott Diabetes Care Inc. | Method and apparatus for providing analyte sensor calibration |
US7768386B2 (en) * | 2007-07-31 | 2010-08-03 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
CA2694931C (en) | 2007-07-31 | 2014-04-29 | Abbott Diabetes Care Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US7731658B2 (en) | 2007-08-16 | 2010-06-08 | Cardiac Pacemakers, Inc. | Glycemic control monitoring using implantable medical device |
US9968742B2 (en) | 2007-08-29 | 2018-05-15 | Medtronic Minimed, Inc. | Combined sensor and infusion set using separated sites |
US20090063402A1 (en) | 2007-08-31 | 2009-03-05 | Abbott Diabetes Care, Inc. | Method and System for Providing Medication Level Determination |
US20090143725A1 (en) | 2007-08-31 | 2009-06-04 | Abbott Diabetes Care, Inc. | Method of Optimizing Efficacy of Therapeutic Agent |
US20090085768A1 (en) | 2007-10-02 | 2009-04-02 | Medtronic Minimed, Inc. | Glucose sensor transceiver |
DE102007047351A1 (en) | 2007-10-02 | 2009-04-09 | B. Braun Melsungen Ag | System and method for monitoring and controlling blood glucose levels |
US8216138B1 (en) | 2007-10-23 | 2012-07-10 | Abbott Diabetes Care Inc. | Correlation of alternative site blood and interstitial fluid glucose concentrations to venous glucose concentration |
US8377031B2 (en) | 2007-10-23 | 2013-02-19 | Abbott Diabetes Care Inc. | Closed loop control system with safety parameters and methods |
US8417312B2 (en) | 2007-10-25 | 2013-04-09 | Dexcom, Inc. | Systems and methods for processing sensor data |
US20090112626A1 (en) | 2007-10-30 | 2009-04-30 | Cary Talbot | Remote wireless monitoring, processing, and communication of patient data |
US7783442B2 (en) | 2007-10-31 | 2010-08-24 | Medtronic Minimed, Inc. | System and methods for calibrating physiological characteristic sensors |
WO2009078905A1 (en) | 2007-12-13 | 2009-06-25 | Cardiac Pacemakers, Inc. | Battery depletion detection in an implantable device |
US9839395B2 (en) | 2007-12-17 | 2017-12-12 | Dexcom, Inc. | Systems and methods for processing sensor data |
US20090164190A1 (en) | 2007-12-19 | 2009-06-25 | Abbott Diabetes Care, Inc. | Physiological condition simulation device and method |
US20090164239A1 (en) | 2007-12-19 | 2009-06-25 | Abbott Diabetes Care, Inc. | Dynamic Display Of Glucose Information |
US20090163855A1 (en) | 2007-12-24 | 2009-06-25 | Medtronic Minimed, Inc. | Infusion system with adaptive user interface |
US20090299155A1 (en) | 2008-01-30 | 2009-12-03 | Dexcom, Inc. | Continuous cardiac marker sensor system |
CA2715624A1 (en) | 2008-02-20 | 2009-08-27 | Dexcom, Inc. | Continuous medicament sensor system for in vivo use |
CA2715628A1 (en) | 2008-02-21 | 2009-08-27 | Dexcom, Inc. | Systems and methods for processing, transmitting and displaying sensor data |
US8396528B2 (en) | 2008-03-25 | 2013-03-12 | Dexcom, Inc. | Analyte sensor |
US20090242399A1 (en) | 2008-03-25 | 2009-10-01 | Dexcom, Inc. | Analyte sensor |
US20090247856A1 (en) | 2008-03-28 | 2009-10-01 | Dexcom, Inc. | Polymer membranes for continuous analyte sensors |
CA2720304C (en) | 2008-04-04 | 2018-05-15 | Hygieia, Inc. | Apparatus for optimizing a patient's insulin dosage regimen |
WO2009126942A2 (en) | 2008-04-10 | 2009-10-15 | Abbott Diabetes Care Inc. | Method and system for sterilizing an analyte sensor |
US7783342B2 (en) | 2008-04-21 | 2010-08-24 | International Business Machines Corporation | System and method for inferring disease similarity by shape matching of ECG time series |
US7938797B2 (en) | 2008-05-05 | 2011-05-10 | Asante Solutions, Inc. | Infusion pump system |
US20100057040A1 (en) | 2008-08-31 | 2010-03-04 | Abbott Diabetes Care, Inc. | Robust Closed Loop Control And Methods |
US9943644B2 (en) | 2008-08-31 | 2018-04-17 | Abbott Diabetes Care Inc. | Closed loop control with reference measurement and methods thereof |
US8734422B2 (en) | 2008-08-31 | 2014-05-27 | Abbott Diabetes Care Inc. | Closed loop control with improved alarm functions |
US20100063372A1 (en) | 2008-09-09 | 2010-03-11 | Potts Russell O | Sweat collection devices for glucose measurement |
EP2345893B1 (en) | 2008-11-04 | 2016-05-04 | Panasonic Healthcare Holdings Co., Ltd. | Measurement device, measurement method, and program |
US9320470B2 (en) | 2008-12-31 | 2016-04-26 | Medtronic Minimed, Inc. | Method and/or system for sensor artifact filtering |
US8974439B2 (en) | 2009-01-02 | 2015-03-10 | Asante Solutions, Inc. | Infusion pump system and methods |
EP4252639A3 (en) | 2009-02-26 | 2024-01-03 | Abbott Diabetes Care Inc. | Method of calibrating an analyte sensor |
EP2410910A4 (en) | 2009-03-27 | 2014-10-15 | Dexcom Inc | Methods and systems for promoting glucose management |
US8062249B2 (en) | 2009-03-31 | 2011-11-22 | Abbott Diabetes Care Inc. | Overnight closed-loop insulin delivery with model predictive control and glucose measurement error model |
EP2448485B1 (en) | 2009-07-02 | 2021-08-25 | Dexcom, Inc. | Analyte sensor |
EP2454587A4 (en) | 2009-07-13 | 2012-12-12 | Freelance Corp | Devices, methods, and kits for determining analyte concentrations |
US8494786B2 (en) | 2009-07-30 | 2013-07-23 | Covidien Lp | Exponential sampling of red and infrared signals |
CA2770581A1 (en) | 2009-08-10 | 2011-02-17 | Diabetes Tools Sweden Ab | Apparatus and method for processing glycemic data |
US8868151B2 (en) | 2009-08-14 | 2014-10-21 | Bayer Healthcare Llc | Electrochemical impedance spectroscopy enabled continuous glucose monitoring sensor system |
WO2011026147A1 (en) | 2009-08-31 | 2011-03-03 | Abbott Diabetes Care Inc. | Analyte signal processing device and methods |
EP2482724A2 (en) | 2009-09-30 | 2012-08-08 | Dexcom, Inc. | Transcutaneous analyte sensor |
US9949672B2 (en) | 2009-12-17 | 2018-04-24 | Ascensia Diabetes Care Holdings Ag | Apparatus, systems and methods for determining and displaying pre-event and post-event analyte concentration levels |
US8579879B2 (en) | 2010-02-19 | 2013-11-12 | Medtronic Minimed, Inc. | Closed-loop glucose control startup |
US20110208027A1 (en) | 2010-02-23 | 2011-08-25 | Roche Diagnostics Operations, Inc. | Methods And Systems For Providing Therapeutic Guidelines To A Person Having Diabetes |
US8543354B2 (en) | 2010-06-23 | 2013-09-24 | Medtronic Minimed, Inc. | Glucose sensor signal stability analysis |
WO2011163519A2 (en) | 2010-06-25 | 2011-12-29 | Dexcom, Inc. | Systems and methods for communicating sensor data between communication devices |
DK2621339T3 (en) | 2010-09-29 | 2020-02-24 | Dexcom Inc | ADVANCED SYSTEM FOR CONTINUOUS ANALYTICAL MONITORING |
EP3744249A1 (en) | 2010-10-27 | 2020-12-02 | Dexcom, Inc. | Continuous analyte monitor data recording device operable in a blinded mode |
US8657746B2 (en) | 2010-10-28 | 2014-02-25 | Medtronic Minimed, Inc. | Glucose sensor signal purity analysis |
US20120165640A1 (en) | 2010-12-23 | 2012-06-28 | Roche Diagnostics Operations, Inc. | Structured blood glucose testing performed on handheld diabetes management devices |
US20140088392A1 (en) | 2011-02-11 | 2014-03-27 | Abbott Diabetes Care Inc. | Feedback from Cloud or HCP to Payer or Patient via Meter or Cell Phone |
EP2685895B1 (en) | 2011-03-17 | 2018-10-10 | University of Newcastle Upon Tyne | System for the self-monitoring and regulation of blood glucose |
US10349871B2 (en) | 2011-08-05 | 2019-07-16 | Dexcom, Inc. | Systems and methods for detecting glucose level data patterns |
US9622691B2 (en) | 2011-10-31 | 2017-04-18 | Abbott Diabetes Care Inc. | Model based variable risk false glucose threshold alarm prevention mechanism |
EP2890297B1 (en) | 2012-08-30 | 2018-04-11 | Abbott Diabetes Care, Inc. | Dropout detection in continuous analyte monitoring data during data excursions |
WO2014052136A1 (en) | 2012-09-26 | 2014-04-03 | Abbott Diabetes Care Inc. | Method and apparatus for improving lag correction during in vivo measurement of analyte concentration with analyte concentration variability and range data |
US9211092B2 (en) | 2013-01-03 | 2015-12-15 | Dexcom, Inc. | End of life detection for analyte sensors |
US9227014B2 (en) | 2013-02-07 | 2016-01-05 | The Board Of Trustees Of The Laland Stanford Junior University | Kalman filter based on-off switch for insulin pump |
US10076285B2 (en) | 2013-03-15 | 2018-09-18 | Abbott Diabetes Care Inc. | Sensor fault detection using analyte sensor data pattern comparison |
-
2006
- 2006-10-25 US US11/552,935 patent/US7630748B2/en active Active
-
2007
- 2007-10-24 CA CA2667930A patent/CA2667930C/en active Active
- 2007-10-24 EP EP07854382A patent/EP2114241A4/en not_active Withdrawn
- 2007-10-24 WO PCT/US2007/082382 patent/WO2008052057A2/en active Application Filing
-
2008
- 2008-09-26 US US12/238,874 patent/US8211016B2/en active Active
-
2009
- 2009-07-20 US US12/506,227 patent/US8216137B2/en active Active
-
2012
- 2012-07-09 US US13/544,946 patent/US9113828B2/en active Active
-
2015
- 2015-08-22 US US14/833,058 patent/US9814428B2/en active Active
-
2017
- 2017-11-10 US US15/808,919 patent/US10194868B2/en active Active
-
2019
- 2019-02-01 US US16/264,747 patent/US11282603B2/en active Active
-
2022
- 2022-03-21 US US17/699,734 patent/US20220208371A1/en active Pending
Non-Patent Citations (1)
Title |
---|
See references of EP2114241A4 * |
Cited By (24)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9882660B2 (en) | 2006-10-26 | 2018-01-30 | Abbott Diabetes Care Inc. | Method, system and computer program product for real-time detection of sensitivity decline in analyte sensors |
US11722229B2 (en) | 2006-10-26 | 2023-08-08 | Abbott Diabetes Care Inc. | Method, system and computer program product for real-time detection of sensitivity decline in analyte sensors |
US10903914B2 (en) | 2006-10-26 | 2021-01-26 | Abbott Diabetes Care Inc. | Method, system and computer program product for real-time detection of sensitivity decline in analyte sensors |
US11102306B2 (en) | 2008-02-21 | 2021-08-24 | Dexcom, Inc. | Systems and methods for processing, transmitting and displaying sensor data |
US9020572B2 (en) | 2008-02-21 | 2015-04-28 | Dexcom, Inc. | Systems and methods for processing, transmitting and displaying sensor data |
US9143569B2 (en) | 2008-02-21 | 2015-09-22 | Dexcom, Inc. | Systems and methods for processing, transmitting and displaying sensor data |
US8579473B2 (en) | 2008-09-12 | 2013-11-12 | Koninklijke Philips N.V. | Luminaire for indirect illumination |
US8979319B2 (en) | 2008-09-12 | 2015-03-17 | Koninklijke Philips N.V. | Luminaire and illumination system |
US9662056B2 (en) | 2008-09-30 | 2017-05-30 | Abbott Diabetes Care Inc. | Optimizing analyte sensor calibration |
US9498165B2 (en) | 2010-02-12 | 2016-11-22 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
US9504430B2 (en) | 2010-02-12 | 2016-11-29 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
US9833199B2 (en) | 2010-02-12 | 2017-12-05 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
US9498164B2 (en) | 2010-02-12 | 2016-11-22 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
US11769589B2 (en) | 2010-02-12 | 2023-09-26 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
US9041730B2 (en) | 2010-02-12 | 2015-05-26 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
US10165986B2 (en) | 2010-02-12 | 2019-01-01 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
US10265030B2 (en) | 2010-02-12 | 2019-04-23 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
US10278650B2 (en) | 2010-02-12 | 2019-05-07 | Dexcom, Inc. | Receivers for analyzing and displaying sensor data |
EP3395252A1 (en) * | 2012-08-30 | 2018-10-31 | Abbott Diabetes Care, Inc. | Dropout detection in continuous analyte monitoring data during data excursions |
US10656139B2 (en) | 2012-08-30 | 2020-05-19 | Abbott Diabetes Care Inc. | Dropout detection in continuous analyte monitoring data during data excursions |
US10942164B2 (en) | 2012-08-30 | 2021-03-09 | Abbott Diabetes Care Inc. | Dropout detection in continuous analyte monitoring data during data excursions |
US10345291B2 (en) | 2012-08-30 | 2019-07-09 | Abbott Diabetes Care Inc. | Dropout detection in continuous analyte monitoring data during data excursions |
US10132793B2 (en) | 2012-08-30 | 2018-11-20 | Abbott Diabetes Care Inc. | Dropout detection in continuous analyte monitoring data during data excursions |
EP2890297A4 (en) * | 2012-08-30 | 2016-04-13 | Abbott Diabetes Care Inc | Dropout detection in continuous analyte monitoring data during data excursions |
Also Published As
Publication number | Publication date |
---|---|
US8216137B2 (en) | 2012-07-10 |
EP2114241A4 (en) | 2010-01-20 |
US9814428B2 (en) | 2017-11-14 |
US20080119708A1 (en) | 2008-05-22 |
US11282603B2 (en) | 2022-03-22 |
US20180064398A1 (en) | 2018-03-08 |
US10194868B2 (en) | 2019-02-05 |
US20090069649A1 (en) | 2009-03-12 |
US9113828B2 (en) | 2015-08-25 |
US8211016B2 (en) | 2012-07-03 |
US20090281407A1 (en) | 2009-11-12 |
US7630748B2 (en) | 2009-12-08 |
US20220208371A1 (en) | 2022-06-30 |
WO2008052057A3 (en) | 2008-08-28 |
US20150366510A1 (en) | 2015-12-24 |
EP2114241A2 (en) | 2009-11-11 |
CA2667930A1 (en) | 2008-05-02 |
US20190159734A1 (en) | 2019-05-30 |
CA2667930C (en) | 2011-04-19 |
US20120277565A1 (en) | 2012-11-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20220208371A1 (en) | Method and System for Providing Analyte Monitoring | |
US10342469B2 (en) | Method and system for dynamically updating calibration parameters for an analyte sensor | |
US9743865B2 (en) | Assessing measures of glycemic variability | |
US20180045707A1 (en) | Analyte Sensor with Lag Compensation | |
US9770211B2 (en) | Analyte sensor with time lag compensation | |
WO2008106645A2 (en) | Method and apparatus for providing rolling data in communication systems |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 07854382 Country of ref document: EP Kind code of ref document: A2 |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2667930 Country of ref document: CA |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
REEP | Request for entry into the european phase |
Ref document number: 2007854382 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2007854382 Country of ref document: EP |