Search Images Maps Play YouTube News Gmail Drive More »
Sign in
Screen reader users: click this link for accessible mode. Accessible mode has the same essential features but works better with your reader.

Patents

  1. Advanced Patent Search
Publication numberUS20030144875 A1
Publication typeApplication
Application numberUS 09/930,632
Publication dateJul 31, 2003
Filing dateAug 15, 2001
Priority dateSep 6, 1997
Also published asUS8239013, US20050251419
Publication number09930632, 930632, US 2003/0144875 A1, US 2003/144875 A1, US 20030144875 A1, US 20030144875A1, US 2003144875 A1, US 2003144875A1, US-A1-20030144875, US-A1-2003144875, US2003/0144875A1, US2003/144875A1, US20030144875 A1, US20030144875A1, US2003144875 A1, US2003144875A1
InventorsStephen Suffin, W. Emory
Original AssigneeSuffin Stephen C., Emory W. Hamlin
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
EEG prediction method for medication response
US 20030144875 A1
Abstract
The present invention includes a system and method for computerized analysis of a patient's electroencephalogram (EEG) recorded by electrodes placed on the scalp, for the purpose of predicting patient response to medications and therapeutic agents commonly used in psychiatric practice. The prediction of the responses to medications (adverse, no effect, favorable outcome) is an important problem in the clinical practice of psychiatry. A growing number of therapeutic agents are available to the clinician but these agents generate variable responses when prescribed based solely on the patient's history and current symptoms. The present invention is used by physicians to improve patient outcome by selecting agents most likely to be effective for a given patient, using a standardized analysis of the digitized EEG and comparison of individual patient EEC data to a particular database of similar patients whose clinical outcome to pharmacotherapy is known.
Images(2)
Previous page
Next page
Claims(8)
We claim:
1. A unique system for compressing, encrypting, tracking, and securely transmitting digital EEG data and associated patient identifying information via the Internet from a remote site to a Report Processing Center, and including the electronic return of a report summarizing results of proprietary analyses and database comparison all without requiring telephonic transmission.
2. Identification of a set of univariate and multivariate EEG features that when observed in a patient diagnosed with a psychiatric disorder, can be used with NuPharm Database's particular rule-based classifier to predict a favorable clinical responsive to psychostimulant class medications.
3. Identification of a set of univariate and multivariate EEG features that when observed in a patient diagnosed with a psychiatric disorder, can be used with NuPharm Database's particular rule-based classifier to predict a favorable clinical responsive to antidepressant class medications.
4. Identification of a set of univariate and multivariate EEG features that when observed in a patient diagnosed with a psychiatric disorder, can be used with NuPharm Database's particular rule-based classifier to predict a favorable clinical response to anticonvulsant class medications.
5. Identification of a set of univariate and multivariate EEG features that when observed in a patient diagnosed with a psychiatric disorder, can be used with NuPharm Database's particular rule-based classifier to predict a favorable clinical responsive to a combination of psychostimulant and antidepressant class medications.
6. Identification of a set of univariate and multivariate EEG features that when observed in a patient diagnosed with a psychiatric disorder, can be used with NuPharm Database's particular rule-based classifier to predict a favorable clinical responsive to a combination of anticonvulsant and antidepressant class medications.
7. Identification of a set of univariate and multivariate EEG features that when observed in a patient diagnosed with a psychiatric disorder, can be used with NuPharm Database's particular rule-based classifier to predict a favorable clinical response to a combination of psychostimulant, antidepressant, and anticonvulsant class medications.
8. A method for computerized generation of clinical reports that integrates interpretive information from medical professionals with results of medication responsivity evaluation according to claim 2.
Description
  • [0001]
    [0001] antidepressant class medications, anticonvulsant class medications, combinations of psychostimulant and antidepressant class medications, combinations of anticonvulsant and antidepressant class medications, combinations of psychostimulant, antidepressant, and anticonvulsant class medications.
  • [0002]
    The present invention also includes a method for computerized generation of clinical reports that integrates interpretive information from medical professionals with results of medication responsivity evaluation.
  • BRIEF DESCRIPTION OF THE FIGURES
  • [0003]
    The present invention may be understood more fully by reference to the following detailed description of the preferred embodiment of the present invention, illustrative examples of specific embodiments of the invention and the appended figures in which
  • [0004]
    [0004]FIG. 1 illustrates a method of the present invention where: step 1 of FIG. 1 corresponds to elements 1 and 2 of the invention described below; step 2 corresponds to elements 3, 4, and 3; step 3 to elements 6 and 7; step 4 to element 8; and step 5 to elements 9 and 10.
  • DETAILED DESCRIPTION OF THE INVENTION
  • [0005]
    More specifically, the following steps are employed:
  • [0006]
    1) The EEG is recorded using electrodes placed on the patient's scalp, and the EEG data is stored in a digital format using a standardized protocol available on one of a number of commercially available instruments (current manufacturers include Cadwell Laboratories, Bio-Logic Systems Corp., Nicolet Biomedical, Oxford Instruments, among others). The International 10-20 System convention is used for determining the location of electrodes placed on the scalp. It is the responsibility of the recording facility to collect data in accordance with procedural specifications.
  • [0007]
    2) The following patient criteria apply:
  • [0008]
    a) Patient must have received a psychiatric diagnosis as specified in the Diagnostic and Statistical Manual, currently the Fourth Edition (DSM-IV).
  • [0009]
    b) Ages between six and ninety.
  • [0010]
    c) Patient is taking no medications. All medications potentially influence the EEG and must be discontinued or avoided for seven half-lives prior to baseline EBG examination. This includes “over the counter” sleeping pills, pain medication, nutritional health supplements and mega-vitamins.
  • [0011]
    d) Insulin, thyroid, estrogen, progesterone and other hormone replacement agents are not excluded. Some cardiac agents are included in the reference population of after the age of fifty-five.
  • [0012]
    e) Patients with any of the characteristics listed below are not suitable for prediction of medication responsivity based on EEG analysis:
  • [0013]
    (i) intramuscular depo-neuroleptic therapy within the preceding twelve months
  • [0014]
    (ii) a history of craniotomy with or without metal prostheses
  • [0015]
    (iii) a history of cerebrovascular accident
  • [0016]
    (iv) spikes or extreme low voltage on the conventional EEG
  • [0017]
    (v) a current diagnosis of seizure disorder
  • [0018]
    (vi) a diagnosis of dementia
  • [0019]
    (vii) mental retardation
  • [0020]
    (viii) current use of marijuana, cocaine, hallucinogens or other drugs of abuse
  • [0021]
    (ix) inability to remain medication-free and drug-free for seven half-lives of the current agent(s) prior to EEG recording
  • [0022]
    (x) significant abnormality of the CBC, chemistry or thyroid panel with TSH until corrected
  • [0023]
    f) A “positive” Urine Drug Screen (UDS) interferes with medication prediction methods. Studies are processed only if the UDS is negative just prior to recording the digital EEG.
  • [0024]
    3) The digital EEG data computer file is packaged along with additional patient identifying information using packaging and transmission software. The patient information includes:
  • [0025]
    a) name
  • [0026]
    b) date of birth
  • [0027]
    c) referring physician
  • [0028]
    d) handedness
  • [0029]
    e) height
  • [0030]
    f) weight
  • [0031]
    g) date of test
  • [0032]
    h) patient ID (social security number)
  • [0033]
    Packaging refers to compression of the computer file and encryption of the file so that it cannot be opened or examined by anyone other than at the processing center. The data transfer is rigorously secured to protect the confidentiality of patient records. The EEG files are encrypted at the recording facility with a key known only to processing center. The patient ID is transformed using a algorithm so that even in the case of mail routing error there is no way to associate the data with an individual. The data is compressed and protected with an additional password and data files are transmitted to a secure site. These steps mean that the patient data are protected against even purposeful attempts to intercept and read them.
  • [0034]
    The transmittal of the EEG file and related patient information is tracked as it is packaged, sent, processed, and returned. All log entries include dates and times calibrated to GMT.
  • [0035]
    The computer operating system preferred to run the packaging and report transmission software is currently Microsoft Windows 95/98. The following hardware and software is preferred:
  • [0036]
    Hardware Requirements
  • [0037]
    Operating System: Windows 95 or Windows 98
  • [0038]
    Processor: 486, 133 MHZ.
  • [0039]
    Monitor and Video Card capable of displaying 256 colors.
  • [0040]
    Disk Space: 35 MB
  • [0041]
    RAM: 16 MB
  • [0042]
    CD-ROM Drive if installing from CD-ROM
  • [0043]
    Modem: 33.6 KBaud
  • [0044]
    Internet Connection with approved Internet Service Provider
  • [0045]
    Software Requirements
  • [0046]
    Adobe Acrobat Reader Version 3.01
  • [0047]
    Microsoft Internet Explorer 4.0 or above
  • [0048]
    The packaging and transmission software
  • [0049]
    4) The computer file is transferred off-hours using standard commercially available file transfer protocols (FTP) via the Internet, to a designated processing site. A special feature of the packaging and transmission software exists to allow immediate transfer of files for priority reporting if requested. The processing site monitors the transfer in order to detect the arrival of new computer files. When a new file is received, it is forwarded for professional interpretation, if requested, and specialized report generation.
  • [0050]
    5) The file is decompressed and decrypted at the processing site. Experienced technical and professional personnel then review the EEG signals and sections of the recording identified as containing signals generated by extracerebral sources are deleted from subsequent analyses. The samples of EEG selected for inclusion in analysis are then passed to the first stage of analysis.
  • [0051]
    6) The first stage of analysis includes computations that extract a standard set of features from the EEG. Quantitative spectral analysis provides commonly used measures of EFG power and relative power. Power is the square of amplitude; amplitude units are in microvolts (μV), power units are microvolts squared(μV2). Relative power is a measure of the proportion of power in a given frequency band compared to the total band power at a given electrode. Frequency bands are defined as delta, 0.5-2.5 Hz.; theta, 2.5-7.5 Hz.; alpha, 7.5-12.5 Hz., and beta, 12.5-32 Hz. The total band is 0.5 to 32 Hz.
  • [0052]
    EEG coherence, a commonly used measure of the similarity of activity for a pair of two scalp electrodes, also is extracted by spectral analysis for all interhemispheric and intrahemispheric sets of electrode pairs, for each frequency band as defined above.
  • [0053]
    Commonly used measures of peak frequency within each defined frequency band are computed.
  • [0054]
    Combinations of power and coherence measures over defined sets of scalp electrodes are also computed.
  • [0055]
    7) Features extracted from individual EEG data by quantitative spectral and statistical analysis are further compared to two distinct databases. In the second stage of analysis, Z-scores representing deviations from a nonsymptomatic reference population are computed. This reference population, often referred to as the “Neurometric” database, contains 2082 quantitative EEG measures including absolute power, relative power, coherence, symmetry, and mean frequency of the delta, theta, alpha and beta frequency bands of the EEG at every electrode position of the International 10-20 System for individuals from 6 to 92 years (database #1). The z-score value obtained by comparison of individual's data to the age appropriate subset of the database represents the patient's statistical deviation from the reference database.
  • [0056]
    8) The third stage of processing involves medication response prediction using the patient database(database #2). This prediction is made by first identifying the pattern of EEG deviations from the reference database. Individual patient deviation is then compared with the characteristic features of the population of patients whose medications and treatment outcomes are known. A rule-based classifier is applied to estimate the likelihood that a patient EEG contains a pattern known to be responsive to a given agent, class of agents, or combination of agents or classes of agents. The EEG variables currently used by the classifier are shown in Tables 1-4, below.
    Column Column
    Heading Description of Abbreviation Heading Description of Abbreviation
    Table 1 Table 2
    RMAD Relative power monopolar FMAD Frequency monopolar
    anterior delta anterior delta
    RMPD posterior data FMPD posterior delta
    RMAT anterior theta FMAT anterior theta
    RMPT posterior theta FMPT posterior theta
    RMAA Anterior alpha FMAA anterior alpha
    RMPA Posterior alpha FMPA posterior alpha
    RMAB Anterior beta FMAB anterior beta
    RMPB posterior beta FMPB posterior beta
    CEAD Coherence interhemispheric AADL Asymmetry intrahemispheric
    anterior delta delta - left
    CEPD Posterior delta AADR delta - right
    CEAT anterior theta AATL theta - left
    CEPT posterior theta AATR theta - right
    CEAA anterior alpha AAAL alpha - left
    CEPA Posterior alpha AAAR alpha - right
    CEAB Anterior beta AABL beta - left
    CEPB posterior beta AABR beta - right
    Table 3 Table 4
    AED Asymmetry monopolar CEBD Coherence interhemispheric bipolar
    interhemispheric delta delta
    AFT Theta CEBT Theta
    AEA Alpha CEBA Alpha
    AEB Beta CEBB Beta
    AEBD Asymmetry bipolar RBDL Relative power bipolar delta left
    interhemispheric delta
    AEBT Theta RBDR Delta - right
    AEBA Alpha RBTL Theta - left
    AEBB Beta RBTR Theta - right
    CADL Coherence intrahemispheric RBAL Alpha - left
    delta - left
    CADR Delta - right RBAR Alpha - right
    CATL Theta - left RBBL Beta - left
    CATR Theta - right RBBR Beta - right
    CAAL Alpha - left
    CAAR Alpha - right
    CABL Beta - left
    CABR Beta - right
  • [0057]
    9) A formal report for the referring clinician is generated. The report is returned in a format that cannot be modified by the client (Adobe Systems, Inc., “portable document format”, or “PDF”). This report contains certain elements as specifically requested by the referring clinician. These elements may include a professional medical interpretation of the digital EEG tracing, a presentation of selected features extracted by quantitative EEG analysis, a presentation of deviations from the Neurometric database, and a statement of the likelihood of favorable pharmacotherapeutic outcome based on comparison with patients having similar EEG features in the patient database #2. The treating physician is responsible for any medication selection, titrating of dosage and monitoring the patient for side effects and is instructed to incorporate results of reports with the psychiatric assessment to develop into an overall clinical treatment plan.
  • [0058]
    10) The report is returned and may be downloaded by the client on a regular schedule, using the packaging and transmission software for viewing and printing the report by the client at the recording site. PDF files are opened and displayed using an interface to Adobe Acrobat Reader (TM) software. Reports may be printed on any operating system compatible printer.
  • [0059]
    11) Follow up EEG recordings can then be used to track changes produced by administration of medications by repeating the entire process outlined above. For follow up studies, the patient also is interviewed by the treating physician and Clinical Global Improvement (CGI) is scored. A score of −1 indicates an adverse effect, 0 no improvement, 1 minimal or mild improvement, 2 moderate improvement, and 3 marked improvement or remission of symptoms. The CGI scores are sent to the processing center and are reported along with changes, expressed as difference scores, on variables shown in Tables 1-4 above.
  • [0060]
    The invention described and claimed herein is not to be limited in scope by the preferred embodiments herein disclosed, since these embodiments are intended as illustrations of several aspects of the invention. Any equivalent embodiments are intended to be within the scope of this invention. Indeed, various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims.
  • [0061]
    The entire disclosures of references cited herein are incorporated herein, in their entireties, for all purposes.
  • [0062]
    Citation or identification of a reference in this application or in connection with this application shall not be construed that such reference is available as prior art to the present invention.
Patent Citations
Cited PatentFiling datePublication dateApplicantTitle
US4201224 *Dec 29, 1978May 6, 1980Roy John EElectroencephalographic method and system for the quantitative description of patient brain states
US5083571 *Apr 18, 1988Jan 28, 1992New York UniversityUse of brain electrophysiological quantitative data to classify and subtype an individual into diagnostic categories by discriminant and cluster analysis
US5176145 *Jan 28, 1991Jan 5, 1993Ryback Ralph SMethod for diagnosing a patient to determine whether the patient suffers from limbic system dysrhythmia
US5230346 *Feb 4, 1992Jul 27, 1993The Regents Of The University Of CaliforniaDiagnosing brain conditions by quantitative electroencephalography
US5267570 *Dec 30, 1992Dec 7, 1993Preston Myra SMethod of diagnosing and treating chronic fatigue syndrome
US5357976 *Dec 21, 1992Oct 25, 1994Genquan FengMethod of and arrangement for diagnosing brain disease
US5445162 *Aug 27, 1993Aug 29, 1995Beth Israel Hospital AssociationApparatus and method for recording an electroencephalogram during magnetic resonance imaging
US5447166 *Jan 19, 1994Sep 5, 1995Gevins; Alan S.Neurocognitive adaptive computer interface method and system based on on-line measurement of the user's mental effort
US5730146 *Feb 9, 1994Mar 24, 1998Itil; Turan M.Transmitting, analyzing and reporting EEG data
US5871517 *Jan 15, 1997Feb 16, 1999Somatics, Inc.Convulsive therapy apparatus to stimulate and monitor the extent of therapeutic value of the treatment
US5873823 *Sep 4, 1996Feb 23, 1999Eidelberg; DavidMarkers for use in screening patients for nervous system dysfunction and a method and apparatus for using same
US5884626 *Aug 31, 1995Mar 23, 1999Toyota Jidosha Kabushiki KaishaApparatus and method for analyzing information relating to physical and mental condition
US6021346 *Jul 23, 1998Feb 1, 2000Electronics And Telecommunications Research InstituteMethod for determining positive and negative emotional states by electroencephalogram (EEG)
US6061593 *Apr 24, 1998May 9, 2000Neuropace, Inc.EEG d-c voltage shift as a means for detecting the onset of a neurological event
US6195576 *Mar 9, 1998Feb 27, 2001New York UniversityQuantitative magnetoencephalogram system and method
US6223074 *Sep 28, 1999Apr 24, 2001Thuris CorporationMethod and computer program product for assessing neurological conditions and treatments using evoked response potentials
US6434419 *Jun 26, 2000Aug 13, 2002Sam Technology, Inc.Neurocognitive ability EEG measurement method and system
US20010020137 *Feb 2, 2001Sep 6, 2001Richard GrangerMethod and computer program product for assessing neurological conditions and treatments using evoked response potentials
Referenced by
Citing PatentFiling datePublication dateApplicantTitle
US7666161May 21, 2007Feb 23, 2010The Spectranetics CorporationThrombectomy and soft debris removal device
US7959608Apr 27, 2004Jun 14, 2011The Spectranetics CorporationThrombectomy and soft debris removal device
US7976528Feb 12, 2010Jul 12, 2011The Spectranetics, Corp.Thrombectomy and soft debris removal device
US8920402Oct 12, 2007Dec 30, 2014The Spectranetics CorporationThrombectomy and soft debris removal device
US20050165323 *May 19, 2004Jul 28, 2005Lamont, Llc.Physiological signal monitoring apparatus and method
US20050240146 *Apr 27, 2004Oct 27, 2005Nash John EThrombectomy and soft debris removal device
US20070282303 *May 21, 2007Dec 6, 2007Nash John EThrombectomy and soft debris removal device
US20080097499 *Oct 12, 2007Apr 24, 2008Nash John EThrombectomy and soft debris removal device
US20090312663 *Dec 17, 2009New York UniversitySystem and Method for Neurometric Analysis
US20100145259 *Feb 12, 2010Jun 10, 2010The Spectranetics CorporationThrombectomy and soft debris removal device
CN103976740A *May 23, 2014Aug 13, 2014清华大学Network environment-oriented electroencephalogram identification system and network environment-oriented electroencephalogram identification method
Classifications
U.S. Classification705/2
International ClassificationA61B5/048, A61B5/04, G06F19/00, G06F21/00, A61B5/16
Cooperative ClassificationG06Q50/22, A61B5/165, G06F19/3418, G06F19/3425, G06F19/345, G06F21/6254, A61B5/16, A61B5/048
European ClassificationA61B5/16H, G06F19/34C, G06F19/34E, G06F21/62B5A, G06Q50/22, A61B5/16, A61B5/048