|Publication number||USRE41291 E1|
|Application number||US 11/232,411|
|Publication date||Apr 27, 2010|
|Filing date||Sep 21, 2005|
|Priority date||May 18, 2001|
|Also published as||EP1389953A1, US6631291, US20020173729, WO2002094099A1|
|Publication number||11232411, 232411, US RE41291 E1, US RE41291E1, US-E1-RE41291, USRE41291 E1, USRE41291E1|
|Inventors||Hanna E. Viertio-Oja, Emmanuel-S Cohen-Laroque|
|Original Assignee||Ge Healthcare Finland Oy|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (34), Non-Patent Citations (27), Referenced by (5), Classifications (38), Legal Events (3)|
|External Links: USPTO, USPTO Assignment, Espacenet|
The present application claims the priority of U.S. provisional application 60/291,873, filed May 18, 2001.
The present invention is directed to a method and apparatus for controlling the administration of an hypnotic drug in “closed loop” fashion.
An hypnotic drug may comprise an anesthetic agent and the hypnotic state induced in a patient by the administration of such a drug in one of anesthetization. An hypnotic drug typically acts on the brain to produce a lessening or loss of consciousness in the patient. The extent to which the patient is anesthetized is often termed the “hypnotic level” or “depth of anesthesia.” In the present invention, the existing hypnotic level, or depth of anesthesia, in the patient is sensed and used to control the hypnotic drug administration to the patient in the manner of a closed loop, or feedback, regulator to achieve and maintain a desired level in the patient.
More particularly, the present invention employs the complexity of electroencephalographic (EEG) data obtained from the patient as a sensed indication of the hypnotic level of the patient for use in controlling hypnotic drug administration. The use of such an indication provides closed loop control of drug administration that is based on an accurate assessment of the hypnotic condition of the patient and one that is highly responsive to changes in that condition. Such an indication can be made rapidly responsive to changes in the hypnotic condition of the patient.
Hypnotic drugs, or anesthetic agents, are administered by inhalation or intravenously. When administration is by inhalation, the anesthetic agent comprises a volatile liquid that is vaporized in a vaporizer. The vaporized anesthetic agent is entrained in breathing gases for the patient. The concentration of the anesthetic agent supplied by the vaporizer is determined by the anesthesiologist by manipulating appropriate controls on the vaporizer. The concentration of anesthetic agent in the lungs of the patient may be measured by measuring the amount of anesthetic agent contained in the breathing gases exhaled by the patient at the end of the exhalation phase of the respiratory cycle, i.e. the end tidal concentration (ETconc). Typical inhaled anesthetic agents are sevoflurane, enflurane, and desflurane, among others.
In a simple form, intravenous administration of an hypnotic drug may employ a syringe that injects the drug into a vein of the patient. For extended administration, a motor driven syringe or a motor driven infusion pump may be employed. A commonly used, intravenously administered, anesthetic agent is propofol.
In addition to hypnosis, high quality anesthesia must also consider loss of sensation (analgesia), muscle relaxation, suppression of the autonomous nervous system, and blockage of the neuro muscular function. This may require administration of a number of different drugs via the same or different routes. Further, different hypnotic drugs and/or different administration routes may be used at different stages of an anesthetization or a medical procedure. For example, hypnosis may be introduced by an intravenously administered drug and maintained by an inhaled drug.
In the process by which a drug, including a hypnotic drug, takes its effect in the body, two aspects are important: pharmacokinetics and pharmacodynamics. Pharmacokinetics Pharmacodynamics deals with the effect of the body on the drug, such as the body's absorption, distribution or diffusion, metabolism, and excretion of the drug. Pharmacokinetics describes how the drug is distributed in the course of time from the site of delivery to different parts of the body and to a particular organ in which the drug is supposed to have its effect.
For use in the study of drugs, the determination of dosages, and the like, mathematical models have been developed for the pharmacokinetics of a drug. Because of the complexity of the physiology of the body, the models are typically based on theoretical compartments, such as plasma, fat, or the brain. Pharmacokinetic models typically allow for consideration of anthropometric data, such as patient height, weight, age, sex, etc. Pharmacokinetic models are available for hypnotic drugs, or anesthetic agents, including propofol, based on two or more different compartments. See Shafer, et al. Anesthesiology, 1998; 69:348-356 describing a two compartment model for propofol.
When a specific effect of a drug can be directly or indirectly measured, such data can be used to define a pharmacodynamic model of the drug with respect to its concentration at the site at which it is effective, i.e. effect-site concentration. Such models may also use anthropometric data. For hypnotic drugs the effect is the hypnotic state of the patient and the effect-site in the brain.
In a broad sense, all hypnotic drug administration is of a controlled loop nature. In a basic form, an anesthesiologist administers such a drug to a patient, observes the state of the patient resulting from the administration of the drug, and then maintains or alters the dose based on his/her observations. However, in a more specific sense, reflecting recent work in the field of anesthesia, closed loop control relates to the sensing of the hypnotic state of the patient by some form of instrumentation and automatically controlling the administration of the drug responsive to a feedback signal from the instrumentation. The term is used herein in the more specific sense.
The interest in closed loop control is posited, at least in part, on a desire to accurately establish the hypnotic level or depth of anesthesia of the patient. If the anesthesia is not sufficiently deep, the patient may maintain or gain consciousness during a surgery, or other medical procedure, resulting in an extremely traumatic experience for the patient, anesthesiologist, and surgeon. On the other hand, excessively deep anesthesia reflects an unnecessary consumption of hypnotic drugs, most of which are expensive. Anesthesia that is too deep requires increased medical supervision during the surgery recovery process and prolongs the period required for the patient to become completely free of the effects of the drug.
Rapidity is another desirable feature of an hypnotic drug administration control system. Fast response is particularly desirable should the patient approach consciousness since, as noted above, unexpected emergence is to be avoided, but is rendered more likely as excessively deep anesthesia is avoided.
A closed loop hypnotic drug delivery system has been described using the bispectral index as a control parameter. See Mortier E., et al. Anesthesia, 1998, August; 53 (8):749-754. See also published European Patent Application No. EP 959,921 to authors of this article. The bispectral index is proprietary to Aspect Medical Systems of Farmingham, Mass. and is described in one or more of the following U.S. Pat. Nos.: 4,907,597; 5,101,891; 5,320,109; and 5,458,117. The bispectral index is an effort to form a single variable, termed the bispectral index (BIS), that correlates behavioral assessments of sedation and hypnosis over a range of anesthesia for several hypnotic drugs.
The bispectral index comprises three components that are combined in various ways to provide an indication over a range of hypnotic levels from light sedation to deep anesthesia. See Ira R. Rampil, “A Primer for EEG Signal Processing in Anesthesia”, Anesthesiology 89 (1998), 980-1002. See also U.S. patent application, Ser. No. 09/688,891 to an inventor named herein and another, assigned to a common assignee, also containing a description of this index.
In order to compute a BIS value, measured EEG data over a period of fifteen seconds is used. During anesthesia, the level of painful stimulation can vary drastically and cause rapid changes in the hypnotic level of the patient, i.e. wake the patient up. Because of the time required to compute a BIS value, the bispectral index may not be sufficiently rapid to warn the anesthesiologist that this is occurring. Also, the bispectral index is contaminated by electromyographic (EMG) activity which may lead to misjudgment of the hypnotic level of a patient. See Bruhn J., et al., Anesthesiology 2000; 92:1485-7. Certain paradoxical behavior of the bispectral index (BIS) not connected to EMG has also been reported; see Detsch O. et al., British Journal of Anesthesia 84 (1):33-7 (2000); Hirota K. et al., Eur J Anaesth 1999, 16, 779-783.
Another approach to closed loop or feedback control of hypnotic drug administration is disclosed in published International Patent Appln. WO 98/10701 by Mantzaridis, et al. In the technique of the patent, the patient is fitted with headphones and is subjected to noise in the form of “clicks” of one ms duration at a frequency of 6.9 Hz. The auditory evoked potential (AEP) resulting from this stimulation, and more particularly, the alteration of the delay between the auditory stimulus and the auditory stem response in the brain is used in this method to evaluate the level of hypnosis of a patient during anesthesia. While an AEP index has been shown to distinguish between the conscious and unconscious states of a patient in an accurate manner, the correlation with drug concentration is not as good and has been reported as poorer than that for the bispectral index. See Doi M, et al., Br J Anaesth. 1997, February; 78(2):180-4. The auditory response does not persist to the lowest hypnotic levels, restricting the range of measurement. This tends to lessen the utility of the AEP index for use in closed loop hypnotic drug administration. Also, the technique requires placing earphones on the patient and is limited to patients having adequate hearing.
U.S. Pat. No. 6,016,444 to E. R. John, describes another method using information extracted from EEG signal data to control a closed-loop drug delivery system. The parameters mentioned include EEG spectral powers measured in different frequency ranges and the spectral edge frequencies, below which are found, for example, 50% or 90% of the total power spectrum. In addition to the EEG spectrum derived parameters, the method also uses brain wave evoked responses, such as brain stem or cortical auditory evoked responses, which may bear a correlation to anesthesia level. Electrodes are applied to the front and back of the scalp and the method essentially compares the derived features between these locations using covariance matrices. After the patient has been anesthetized and when he/she has obtained the plane of anesthesia desired by the anesthesiologist, a form of calibration procedure called “self-normalization” is carried out. The plane of anesthesia is determined by clinical signals observed by the anesthesiologist. After self-normalization, the system tries to maintain the anesthetic level of the patient established during that procedure as the set point.
The need for the self-normalization procedure presents a disadvantage to this procedure in that the anesthesiologist may forget to carry it out or carry it out at the wrong plane of anesthesia. In the time period required for the procedure, which according to the patent preferably lasts for 60 seconds, the condition of the patient may change. Also, there is no published evidence that the particular EEG-derived parameters chosen for measurement correlate very well with hypnotic levels.
It is, therefore, an object of the present invention to provide an improved method and apparatus for controlling the administration of an hypnotic drug to a patient in closed loop fashion that employs an accurate and highly responsive indication of the hypnotic condition of the patient, thereby to improve the administration of the drug. The indication used in the present invention can be made rapidly responsive to changes in the hypnotic condition of the patient. This is particularly advantageous in alerting an anesthesiologist that the patient may be emerging from an anesthetized state to a conscious state.
It is a further object of the present invention to provide a closed loop control method and apparatus which is capable of operating over a wide range of hypnotic conditions in the patient ranging from no hypnosis, i.e. consciousness, to deep hypnosis or anesthesia.
The method and apparatus of the present invention is simple to set up, employing a simple array of electrodes on the head of the patient. No self-normalization procedure as required in earlier disclosed techniques, is required with the technique of the present invention.
Briefly, in the present invention, electroencephalographic (EEG) signal data is obtained from the patient. For this purpose, one or more pairs of biopotential electrodes may be applied to the forehead of the patient. At least one measure of the complexity of the EEG signal data is derived from the data. The complexity measure of the EEG signal data may comprise the entropy of the EEG signal data. An EEG signal complexity measure obtained from the cerebral activity of the patient can be advantageously used in conjunction with a measure of patient electromyographic (EMG) activity resulting from the muscle activity of the patient to improve the response time of hypnotic level determination and of the feedback control of drug administration. The EEG signal data complexity measure is used in as the feedback signal in a control loop for an anesthetic delivery unit to control hypnotic drug administration to the patient in a manner that provides the desired hypnotic level in the patient.
A plurality of EEG signal data complexity measures may be used in determining the hypnotic level of the patient, if desired.
To improve the control of hypnotic drug administration, the present invention may employ a transfer function relating to the pharmacological effects of the drug in the patient and the manner, or other characteristics of, its administration. Pharmacokinetic and pharmacodynamic models may be employed in establishing the transfer function.
The control of drug administration provided by the present invention may be improved by the use of additional data obtained from the patient, such as his/her cardiovascular characteristics or the end tidal concentration of volatile hypnotic drugs.
Information pertinent to the anesthetization of the patient, such as patient characteristics, hypnotic drug type, particular medical procedure and physician, may be inputted or stored for use in carrying out the control of drug administration. Information generated during course of an anesthetization may also be employed in controlling the administration of the hypnotic drug to the patient.
Various other features, objects, and advantages of the invention will be made apparent from the following detailed description and the drawings.
In the drawing:
In the present invention, a quantification of the complexity of the EEG signals obtained from the patient is used to determine his/her hypnotic level and, in turn, to control the administration of a hypnotic drug to the patient in a closed loop fashion. This approach is based on the premise that neuronal systems, such as those of the brain, have been shown to exhibit a variety of non-linear behaviors so that measures based on the non-linear dynamics of the highly random EEG signal allow direct insight into the state of the underlying brain activity. EEG biopotential signals are obtained from electrodes applied to the head of the patient.
There are a number of concepts and analytical techniques directed to the complex nature of random and unpredictable signals. One such concept is entropy. Entropy, as a physical concept, describes the state of disorder of a physical system. When used in signal analysis, entropy addresses and describes the complexity, unpredictability, or randomness characteristics and information content of a signal. In a simple example, a signal in which sequential values are alternately of one fixed magnitude and then of another fixed magnitude has an entropy of zero, i.e. the signal is totally predictable. A signal in which sequential values are generated by a random number generator has greater complexity and a higher entropy.
Applying the concept of entropy to the brain, the premise is that when a person is awake, the mind is full of activity and hence the state of the brain is more nonlinear, complex, and noise like. Since EEG signals reflect the underlying state of brain activity, this is reflected in relatively more “randomness” or “complexity” in the EEG signal data, or, conversely, in a low level of “order.” As a person falls asleep or is anesthetized, the brain function begins to lessen and becomes more orderly and regular. As the activity state of the brain changes, this is reflected in the EEG signals by a relative lowering of the “randomness” or “complexity” of the EEG signal data, or conversely, increasing “order” in the signal data. When a person is awake, the EEG data signals will have higher entropy and when the person is asleep the EEG signal data will have a lower entropy.
With respect to anesthesia, an increasing body of evidence shows that EEG signal data contains more “order”, i.e. less “randomness”, and lower entropy, at higher concentrations of an hypnotic drug, i.e. a lower hypnotic level or greater depth of anesthesia, than at lower concentrations. At a lower concentration of hypnotic drug, the EEG signal has higher entropy. This is due, presumably, to lesser levels of brain activity in the former state than in the latter state. See “Stochastic complexity measures for physiological signal analysis” by I. A. Rezek and S. J. Roberts in IEEE Transactions on Biomedical Engineering, Vol. 4, No. 9, September 1998 describing entropy measurement to a cut off frequency of 25 Hz and Bruhn J, et al. “Approximate Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect during Desflurane Anesthesia”, Anesthesiology, 92 (2000), pgs. 715-726 describing entropy measurement in a frequency range of 0.5 to 32 Hz. See also Viertiö-Oja H, et al. “New method to determine depth of anesthesia from EEG measurement” in J. Clin. Monitoring and Comp. Vol. 16 (2000) pg. 60 which reports that the transition from consciousness to unconsciousness takes place at a universal critical value of entropy which is independent of the patient. See also Zhang XS et al., Med. Bio. Eng. Comput. 1999, 37:327-34.
In sum, the following can be said. First, certain forms of entropy have generally been found to behave consistently as a function of hypnotic or anesthetic depth. See Bruhn J, et al. Anesthesiology 92 (2000) 715-26; Anesthesiology 93 (2000) 981-5 and Viertiö-Oja H, et al. “Entropy of EEG signal is a robust index for depth of hypnosis”, Anesthesiology 93 (2000) A, pg. 1369. This warrants consideration of entropy as a natural and robust choice to characterize levels of hypnosis. Also, because entropy correlates with depth of anesthesia at all levels of anesthesia, it avoids the need to combine various subparameters as in the bispectral index (BIS). Second, it has been found that the transition from consciousness to unconsciousness takes place at a critical level of entropy which is independent of the patient. See Viertiö-Oja H, et al. in J. Clin. Monitoring and Computing, Vol. 16 (2000) pg. 60. Thirdly, and of particular practical significance, recovery of a patient toward consciousness from anesthesia can often be predicted by a rise of entropy toward the critical level.
A number of techniques and associated algorithms are available for quantifying signal complexity, including those based on entropy, as described in the Rezek and Roberts article in IEEE Transactions on Biomedical Engineering. One such algorithm is that which produces spectral entropy for which the entropy values are computed in frequency space. Another algorithm provides approximate entropy which is derived from the Kolmogorov-Sinai entropy formula and computed in Taken's embedding space. See Steven M. Pincus, Igor M. Gladstone, and Richard A. Ehrenkranz, “A regularity statistic for medical data analysis”, J. Clin. Monitoring 7 (1991), pgs. 335-345. A program for computing approximate entropy is set out in the Bruhn et al., article in Anesthesiology. The spectral entropy and approximate entropy techniques have found use in analyzing the complexity of EEG signals.
Another technique for non-linear analysis of highly random signals is expressed in Lempel-Ziv complexity in which the complexity of a string of data points is given by the number of bytes needed to make the shortest possible computer program which is able to generate the string. See Abraham Lempel and Jacob Ziv, “On the complexity of finite sequences”, IEEE Trans., IT-22 (1976), pgs. 75-81.
A still further approach that may be applied to EEG signal analysis is fractal spectrum analysis based on chaos theory. In fractal spectrum analysis, the EEG signal is divided into a harmonic component and a fractal component. The harmonic component includes the simple frequencies whereas the fractal component contains the part which is invariant under scaling in time. It has been found that the fractal exponent Beta which corresponds to the frequency power law 1/fβ increases consistently in the course of deepening anesthesia. See Vierti0-Oja, H. et al. in J. Clinical Monitoring and Computing, Vol. 16 (2000), pg. 16.
The use of spectral entropy to characterize the amount of complexity or disorder in an EEG signal is deemed advantageous because of its computational simplicity. The use of spectral entropy to obtain a diagnostic index indicative of the depth of anesthesia of hypnotic level of a patient is described in detail in the aforesaid U.S. patent application 09/688,891 which is incorporated herein by reference in its entirety.
The complexity measurement derived from EEG signal data can be combined with a more rapidly obtainable measure derived from electromyographic (EMG) signals. EMG signals result from the activity of the muscles and exist as long as the muscles are not paralyzed. With the measurement of electromyographic (EMG) activity contained in the biopotentials from electrodes on the forehead of the patient, as the level of anesthesia approaches inadequacy, a painful stimulus to the patient causes a contraction of the frontalis muscle (frowning) which can be detected as peaks in EMG signal amplitude. This reaction can often be observed substantially before the pain eventually brings the patient to consciousness. EMG signals can thus provide an early warning sign to the anesthesiologist to increase the administration of hypnotic drug(s) in order to prevent consciousness and awareness during surgery. The measure derived from the EMG signals may comprise spectral power data.
Both the EEG and EMG signals are typically obtained from the same set of electrodes applied, for example, to the forehead of the patient so that the signals from the electrodes contain both types of data. The EEG signal component dominates the lower frequencies (up to about 30 Hz) contained in the biopotentials existing in the electrodes and EMG signal component dominates the higher frequencies (about 50 Hz and above).
Importantly, because of the higher frequency of the EMG signals, the sampling time can be significantly shorter than that required for the lower frequency EEG signals. This allows the EMG data to be computed more frequently so that a combined EEG-EMG diagnostic indicator of hypnotic level or depth of anesthesia can quickly indicate changes in the state of the patient.
In one approach to providing such a diagnostic index, the EEG signals and the EMG signals can be separately analyzed and thereafter combined into the diagnostic index or indicator. As noted above, because of the celerity with which changes in the anesthetic state of the patient can be determined from the EMG signals, the overall index can quickly inform the anesthesiologist of changes in the state of the patient. For example, the response time for computing the hypnotic level of the patient from the complexity of the EEG signal is approximately 5-30 seconds whereas the data derived from the EMG signal and the diagnostic index can be fully updated every 0.5 seconds.
In another approach, the spectral range of the complexity computations, i.e. entropy computations, is widened to extend into the EMG range. Thus, the spectral range over which the complexity computations are carried out to provide an indicator may extend from some lower frequency of, for example 0.5 to 7 Hz, up to a frequency above 32 Hz. To filter out power line interference, the spectral range may be divided into bands with the elimination of frequencies around 50, 60 Hz and 100, 120 Hz. For example, in an embodiment in which the spectral range extends to approximately 150 Hz, a lower frequency band (0.5-47 Hz) will contain mostly EEG activity while two upper bands (63-97 Hz and 123-147 Hz) will include primarily EMG activity. The use of a widened frequency range does not require a division of the spectrum into two segments as does the first approach because all components in the widened frequency range are treated in the same manner. And, any boundary within the spectral range would be artificial since the frequency bands for the EEG and EMG signals are overlapping.
Further, the complexity measurement obtained in this second approach can be updated as often as is permitted by the higher frequencies of the EMG signals in the widened spectral range of the complexity computation. This will provide a very current indication to the anesthesiologist of the depth of anesthesia of the patient.
The indicator obtained from the signal complexity computation over the widened spectral range can be used in conjunction with a complexity measurement obtained only from the EEG portions of the frequency spectrum to provide useful information to the anesthesiologist regarding what portion of the indicator comes from cerebral activity and what portion comes from muscle activity. This is particularly important in cases in which muscle tension is enhanced for some reason. An example that is frequently encountered is with opioid anesthesia that is often used in heart operations. The extensive use of opioids has the side effect of high muscle rigidity that persists after loss of consciousness. If the BIS is used, this results in misleadingly high values of the BIS. Distinction of the complexity measurement obtained only from the EEG portions of the frequency spectrum from the signal complexity over the widened spectral range shows this situation clearly.
The hypnotic drug may be supplied to patient 12 by anesthesia delivery unit 14. If the drug is administered intravenously anesthetic delivery unit 14 may comprise a motor driven infusion pump. For hypnotic drugs administered by inhalation, anesthesia delivery unit 14 is typically a vaporizer. As noted above, it is common to use both types of hypnotic drugs and differing anesthetic delivery units in the course of an anesthetization. The amount of hypnotic drug delivered by anesthetic delivery unit 14 is controlled by control unit 16, typically by controlling its infusion or administration rate.
To determine the hypnotic state existing in patient 12, electrodes 20 may be applied to the forehead of patient 12 as shown in FIG. 2. Electrodes 20 receive electroencephalographic (EEG) signals from patient 12. The electrodes also receive electromyographic (EMG) signals from the forehead of patient 12. Electrodes 20 are connected to conductors 22 which may be formed into cable 24.
Cable 24 is connected to EEG complexity determination unit 26. Unit 26 includes a protection circuit which is operative in the event the patient is subjected to electro-surgery or cardiac defibrillation, an analog digital converter, and a bandpass filter. Unit 24 26 also contains one or more computational elements, such as a microprocessor, that performs artifact detection and removal and determines the spectral entropy or other characterization of the amount of complexity or disorder in the EEG signal obtained from electrodes 20, as well as spectral power data derived from the EMG signal data obtained from the electrodes, thereby to provide EEG signal data.
The output of EEG complexity determination unit 26 comprises a diagnostic index or other value indicative of the complexity or disorder of the EEG signal data. As noted above, it is deemed preferable for reasons of reducing response times, particularly in sensing the emergence of the patient from the hypnotic state, to incorporate data from EMG signals in such a diagnostic index or value. It may also be advantageous to provide more than one index. For example, indices in which signal complexities have has been computed over different frequency ranges may be used. The output from EEG complexity determination unit is provided to a further input of control unit 16 as shown in
In a simple embodiment of the invention shown in
The hypnotic level existing in patient 12, as ascertained by EEG complexity determination unit 26, is driven toward that corresponding to the input signal from input device 18 by the action of the control loop in control 10 in the well known manner of a closed loop or feedback regulator. The polarity of the reference and feedback inputs to comparator 26 28 are shown in
As shown in
Also as shown in
As further shown in
In the embodiment of the invention schematically shown in
Pharmacokinetic model 52 allows the hypnotic drug to be administered in such a way that its relative concentration in a given compartment, i.e. the brain, can be maintained generally stable, or constant at that which produces the desired hypnotic level. This stability brings a major advantage for both the patient and the anesthesiologist since once an efficient level of drug effect has been reached, the drug level, and hence the hypnotic level will remain constant, thereby to avoid changes in the patient condition, such as regaining consciousness. However, since an hypnotic drug's real effect cannot be fully predicted for a given patient due to pharmacogenetics and because of the variability among individuals of pharmacokinetics models, the use of pharmacodynamic model 54, in addition to pharmacokinetic model 52 and the determination of EEG signal data complexity by unit 26 allows for both the determination of the appropriate effect-site concentration, i.e. the concentration to achieve a given hypnotic level and hence EEG signal data complexity level, as well as a steady state drug level. Where needed for both the models, the “effect” of the hypnotic drug can be measured by evaluating the complexity of the EEG signal data, particularly that originating from the cerebral portion of the EEG signal data.
Also, as shown in
Programmed data in source 56 may also include timing data. This data may be used by control unit 16b to establish a stable, set complexity level for the EEG data signal, and hence hypnotic level in patient 12, for a predetermined period of time. Or, the programmed data may be such that the anesthesiologist could operate program data source 58 56 so that control 10 is operated in a manner to wake the patient after a preset time as for example, by setting up a “wake-up after ten minutes” routine in source 56. Responsive to inputs provided from data source 56, control logic 30a would then establish the required drug administration rates and timing for anesthetic delivery unit 14 to patient 12 to obtain this effect and timing. An analogous procedure could be carried out with respect to the administration of the hypnotic drug to induce unconsciousness, i.e. loss of consciousness in patient 12 at a point in time in the future. Such features are advantageous for cost savings in terms of operating room usage times, amounts of drug used, and the like.
The transfer function generator 50, as well as models 52, 54, may be supplied with information from a database storage device 58. Such a storage device will typically retain reusable data, such as standard data or stored patient data inputted to the storage device or inputted, or developed by control 10. This will enable patient data obtained during a prior anesthetization to be reused should the patient require a subsequent anesthetization with the same drug. If desired, transfer function generator 58 50 may also store information of the type described above in connection with source 56, such as patient type, nature of the surgery, surgical intensity, patterns, drug interaction, etc.
Also, control 10 can record a time series of measured and computed patient information to compute, after enough data is recorded, a patient's specific profile that, thereafter, can be used to predict the behavior of the patient for any particular change of drug delivery rate, as by use of models 52 and 54.
It will be appreciated that, for safety reasons, the control will include appropriate means to allow the anesthesiologist to manually control the delivery of the hypnotic agent, by operation of an input device, by direct intervention at the anesthetic delivery unit, or in same other effective manner.
It is recognized that other equivalents, alternatives, and modifications aside from those expressly stated, are possible and within the scope of the appended claims.
|Cited Patent||Filing date||Publication date||Applicant||Title|
|US2690178||Nov 13, 1950||Sep 28, 1954||Research Corp||Automatic apparatus for administering drugs|
|US4417590||Nov 6, 1980||Nov 29, 1983||Beckman Instruments, Inc.||Electroencephalograph|
|US4421122||May 15, 1981||Dec 20, 1983||The Children's Medical Center Corporation||Brain electrical activity mapping|
|US4533346||Aug 25, 1982||Aug 6, 1985||Pharmacontrol Corporation||System for automatic feedback-controlled administration of drugs|
|US4705049||Aug 4, 1986||Nov 10, 1987||John Erwin R||Intraoperative monitoring or EP evaluation system utilizing an automatic adaptive self-optimizing digital comb filter|
|US4753246||Mar 28, 1986||Jun 28, 1988||The Regents Of The University Of California||EEG spatial filter and method|
|US4907597||Oct 9, 1987||Mar 13, 1990||Biometrak Corporation||Cerebral biopotential analysis system and method|
|US5010891||Apr 12, 1989||Apr 30, 1991||Biometrak Corporation||Cerebral biopotential analysis system and method|
|US5109862||Feb 21, 1991||May 5, 1992||Del Mar Avionics||Method and apparatus for spectral analysis of electrocardiographic signals|
|US5320109||Oct 25, 1991||Jun 14, 1994||Aspect Medical Systems, Inc.||Cerebral biopotential analysis system and method|
|US5458117||Jun 9, 1994||Oct 17, 1995||Aspect Medical Systems, Inc.||Cerebral biopotential analysis system and method|
|US5474082||Jan 6, 1993||Dec 12, 1995||Junker; Andrew||Brain-body actuated system|
|US5566678||Jan 5, 1995||Oct 22, 1996||Cadwell Industries, Inc.||Digital EEG noise synthesizer|
|US5579774||Mar 7, 1994||Dec 3, 1996||Camino Neurocare, Inc.||Method and apparatus for monitoring local cerebral physiology|
|US5769793||Sep 19, 1996||Jun 23, 1998||Steven M. Pincus||System to determine a relative amount of patternness|
|US5816247||Jun 13, 1996||Oct 6, 1998||Rdm Consultants Ltd.||Monitoring an EEG|
|US5846208||Aug 22, 1997||Dec 8, 1998||Siemens Aktiengesellschaft||Method and apparatus for the evaluation of EEG data|
|US5857978||Mar 20, 1996||Jan 12, 1999||Lockheed Martin Energy Systems, Inc.||Epileptic seizure prediction by non-linear methods|
|US5995868||Jan 6, 1997||Nov 30, 1999||University Of Kansas||System for the prediction, rapid detection, warning, prevention, or control of changes in activity states in the brain of a subject|
|US6016444||Dec 10, 1997||Jan 18, 2000||New York University||Automatic control of anesthesia using quantitative EEG|
|US6061593||Apr 24, 1998||May 9, 2000||Neuropace, Inc.||EEG d-c voltage shift as a means for detecting the onset of a neurological event|
|US6067467||Dec 21, 1998||May 23, 2000||New York University||EEG operative and post-operative patient monitoring method|
|US6117066||Sep 23, 1998||Sep 12, 2000||Somatics, Inc.||Prevention of seizure arising from medical magnetoictal non-convulsive stimulation therapy|
|US6128094 *||Jul 8, 1998||Oct 3, 2000||Hewlett-Packard Company||Printer having processor with instruction cache and compressed program store|
|US6138668 *||Nov 26, 1997||Oct 31, 2000||Inhale Therpeutic Systems||Method and device for delivering aerosolized medicaments|
|US6594524||Dec 12, 2000||Jul 15, 2003||The Trustees Of The University Of Pennsylvania||Adaptive method and apparatus for forecasting and controlling neurological disturbances under a multi-level control|
|US6678548 *||Oct 20, 2000||Jan 13, 2004||The Trustees Of The University Of Pennsylvania||Unified probabilistic framework for predicting and detecting seizure onsets in the brain and multitherapeutic device|
|US6731975||Oct 16, 2000||May 4, 2004||Instrumentarium Corp.||Method and apparatus for determining the cerebral state of a patient with fast response|
|US20020061540 *||Nov 21, 2001||May 23, 2002||Lion Bioscience Ag||Method for screening and producing compound libraries|
|GB2113843A *||Title not available|
|GB2113846A||Title not available|
|WO1997034648A1||Mar 14, 1997||Sep 25, 1997||Masson Jean Pierre||Apparatus for controlling an intravenous injection|
|WO1998010701A1||Sep 10, 1997||Mar 19, 1998||Univ Glasgow||Anaesthesia control system|
|WO2002032305A1||Oct 12, 2001||Apr 25, 2002||Instrumentarium Corp||Method and apparatus for determining the cerebral state of a patient with fast response|
|1||A Primer for EEG Signal Processing in Anesthesia, Ira J. Rampil, M.S., M.D., Anesthesiology, vol. 89, No. 4, Oct. 1998, pp. 980-1002.|
|2||A Regularity Statistic for Medical Data Analysis, Steven M. Pincus, PhD, et al., Journal of Clinical Monitoring, vol. 7, No. 4, Oct. 1991, pp. 335-345.|
|3||Amendment dated Jan. 10, 2006 filed in European Patent Application 2727903.3.|
|4||Approximate Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect During Desflurane Anesthesia, Jorgen Bruhn, M.D., et al., Anesthesiology, vol. 92, No. 3, Mar. 2000, pp. 715-726.|
|5||Closed-loop controlled administration of propofol using bispectral analysis, E. Mortier et al.; 1999 Anaesthesia, 1998, vol. 53, pp. 749-754.|
|6||Development Equations for the Electroencephalogram, E.R. John, H. Ahn, L. Prichep, T. Trepetin, D. Brown, and H. Kaye, Science, 10980, 210: 1255-1258.|
|7||Electroencephalogram Approximate Entropy Correctly Classifies the Occurrence of Burst Suppression Pattern as Increasing Anesthetic Drug Effect, Jörgen Bruhn, M.D. et al., Anesthesiology vol. 93, No. 4, Oct. 2000, pp. 981-985.|
|8||Electromyographic Activity Falsely Elevates the Bispectral Index, Jörgen Bruhn, M.D. et al., Anesthesiology, vol. 92, No. 5, May 2000, pp. 1485-1487.|
|9||Entropy of the EEG Signal is a Robust Index for Depth of Hypnosis, Hanna E. Viertiö-Oja et al., 2000 ASA Meeting Abstracts, pp. 1-2.|
|10||European Amendment dated Dec. 12, 2006.|
|11||European Patent Office Action of Aug. 27, 2007.|
|12||European Patent Office Action of Jun. 12, 2006.|
|13||Increasing isoflurane concentration may cause paradoxical increases in the EEG bispectral index in surgical patients, O. Detsch et al., British Journal of Anaesthesia 2000, 84(1): pp. 33-37.|
|14||International Search Report in WO 02/094099-also use din European Patent Application 2727903.3.|
|15||International Search Report in WO 02/094099—also use din European Patent Application 2727903.3.|
|16||New Method to Determine Depth of Anesthesia From EEG Measurements, H. E.. Viertiö-Oja et al., Journal of Clinical Monitoring and Computing, vol. 16, No. 1, Jan. 2000, p. 60.|
|17||Office Action of European Patent Office, Mar. 21, 2005.|
|18||On the Complexity of Finite Sequences, Abraham Lempel, et al., IEEE Transactions on Information Theory, vol. IT-22, No. 1, Jan. 1976, pp. 75-81.|
|19||Onset of Propofol-Induced Burst Suppression May Be Corrected Detected as Deepening of Anaesthesia by Approximate Entropy, But Not by Bispectral Index, Br. J. Anaesth. Sep. 2001; 87(3):505-7 by Bruhn Jr., Bouillon, T.W., Shafer, S.L..|
|20||Phamacokinetics and Phamacodynamics of Propofol Infusions during General Anaesthesia, Audrey Shafer, M.D. et al., Anesthesiology, vol. 69, pp. 348-356, 1988.|
|21||Predicting movement during anaesthesia by complexity analysis of electroencephalograms, X.-S. Zhang et al., Medical and Biological Engineering & Computing, 1999, vol. 37, pp. 327-334.|
|22||Quantification of EEG Irregularity by Use of the Entropy of the Power Spectrum, T. Inouye, K. Shinosaki, H. Sakamotor, S. Toi, S. Ukai, A. Iyama, Y. Katsuda and M. Hirano, Electroencephalography and Clinical Neurophysiology, 70 (1191) 204-210.|
|23||Relationship between calculated blood concentration of propofol and electrophysiological variables during emergence from anaesthesia: comparison of bispectral index, spectral edge frequency, median frequency and auditory evoked potential index, M. Doi et al., British Journal of anaesthesia 1997, vol. 78, pp. 180-184.|
|24||Stochastic Complexity Measures for Physiological Signal Analysis, I.A. Rezek et al., IEEE Transactions on Biomedical Engineering, vol. 45, No. 9, Sep. 1998, pp. 1186-1191.|
|25||The effects of nitrous oxide and ketamine on the bispectral index and 95% spectral edge frequency during propofol-fentanyl anaesthesia, K. Hirota et al., European Journal of Anaesthesiology 1999, vol. 16, pp. 779-783.|
|26||Theoretical Electroencephalogram Stationary Spectrum for a White-noise-driven Cortex: Evidence for a General Anesthetic-Induced Phrase Transition, Moira I. Steyn-Ross and D.A. Steyn-Ross et al. 1999 The American Physical Society, Physical Review E, vol. 60, No. 6, Dec. 1999, pp. 7299-7310.|
|27||*||U.S. Appl. No. 09/688,891, Viertiö-Oja et al., filed Oct. 2000.|
|Citing Patent||Filing date||Publication date||Applicant||Title|
|US7920914 *||Feb 17, 2009||Apr 5, 2011||Yuan Ze University||Method for monitoring the depth of anesthesia|
|US8864702 *||Dec 4, 2009||Oct 21, 2014||Hopital Foch||System for controlling means for injection of anesthetics or sedatives|
|US9089642||Jun 20, 2012||Jul 28, 2015||Renaudia Medical, Llc||Distributed medication delivery system and method having autonomous delivery devices|
|US20090177108 *||Feb 17, 2009||Jul 9, 2009||Yuan Ze University||Method for Monitoring the Depth of Anesthesia|
|US20110295196 *||Dec 4, 2009||Dec 1, 2011||Hopital Foch||System for controlling means for injection of anesthetics or sedatives|
|U.S. Classification||600/544, 600/546|
|International Classification||A61M16/18, A61B5/11, A61B5/0476, A61B5/048, A61M21/02, A61M5/172, A61M16/10, A61B5/0488, A61M5/168, A61B5/04|
|Cooperative Classification||A61B5/0476, A61B5/0488, A61M2230/60, A61B2505/05, A61M5/1723, A61M2230/30, A61M16/18, A61M2016/1035, A61B5/6814, A61M2230/04, A61M2230/10, A61B5/1106, A61B5/4821, A61M2230/437, A61B5/048, A61M16/01, A61M2202/0241, A61M2230/205|
|European Classification||A61M16/01, A61B5/48D, A61B5/048, A61M5/172B, A61B5/0476, A61M16/18, A61B5/0488, A61B5/11H4|
|Nov 20, 2009||AS||Assignment|
Owner name: GE HEALTHCARE FINLAND OY,FINLAND
Free format text: DEMERGER PLAN;ASSIGNOR:INSTRUMENTARIUM CORP.;REEL/FRAME:023548/0841
Effective date: 20041231
|Apr 7, 2011||FPAY||Fee payment|
Year of fee payment: 8
|Apr 7, 2015||FPAY||Fee payment|
Year of fee payment: 12