EEG prediction method for medication response

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.

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.