System and Method for Assessing Efficacy of Therapeutic Agents

• US 20100286549A1
• Filed: 12/12/2008
• Published: 11/11/2010
• Est. Priority Date: 12/18/2007
• Status: Abandoned Application

• Current Assignee: New York University
• Original Assignee: New York University
• Inventors: Prichep, Leslie S., John, Erwin Roy
• Application Number:
  US12/744,549
• Publication Number:
  US 20100286549A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/4833 Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00

Abstract

A method for assessing an effect of a therapeutic agent, comprises the steps of detecting brain electrical activity of a subject to generate a first set of brain wave data and extracting from the first set of brain wave data first data features sensitive to a neurological disorder in combination with the steps of comparing the first data features to control data to define a baseline profile of brain electropathophysiology and computing one of a first classifying score and a first discriminant score based on the baseline profile to estimate a probability that the baseline profile corresponds to a predetermined pathophysiological condition.

Claims

1. A method for assessing an effect of a therapeutic agent, comprising the steps of:

detecting brain electrical activity of a subject to generate a first set of brain wave data;

extracting from the first set of brain wave data first data features sensitive to a neurological disorder; and

comparing the first data features to control data to define a baseline profile of brain electropathophysiology; and

computing one of a first classifying score and a first discriminant score based on the baseline profile to estimate a probability that the baseline profile corresponds to a predetermined pathophysiological condition.

2. The method according to claim 1, further comprising:

administering a dosage of a therapeutic agent to the subject; and

after a predetermined time interval has elapsed since the administration of the dosage, detecting brain electrical activity of the subject to generate a second set of brain wave data, the predetermined time interval being determined based on pharmocokinetic properties of the therapeutic agent.

3. The method according to claim 2, further comprising:

subjecting the second set of brain wave data to spectral analysis to select second data features sensitive to a neurological disorder; and

comparing the second data features to control data to define a post-treatment profile of brain electropathophysiology; and

computing one of a second classifying score and a second discriminant score based on the post-treatment profile to evaluate an effect of the therapeutic agent.

4. The method according to claim 1, wherein the control data includes population norm data, the method further comprising:

compiling first population brain wave data from each of a first plurality of individuals, the first population brain wave data including data corresponding to the first data features;

sorting the first population brain wave data based on criteria including at least one of age, medical history, gender and ethnicity of the first plurality of individuals; and

selecting portions of the first population brain wave data for use as the population norm data based on a comparison of the criteria and corresponding characteristics of the subject.

5. The method according to claim 4, wherein the first plurality of individuals are selected from a group including one of individuals not suffering from the neurological disorder, individuals with a form of the neurological disorder more manageable than that of the subject and individuals substantially free of symptoms of the neurological disorder.

6. The method according to claim 4, further comprising:

compiling second population brain wave data from each of a second plurality of individuals, the second population brain wave data including data corresponding to the first data features;

sorting the second population brain wave data based on second criteria including at least one of a therapeutic agent used to treat the disorder of the corresponding individual and an observed therapeutic effect of the agent in treating the disorder of the corresponding individual;

selecting portions of the second population brain wave data for use as the population norm data based on a comparison of the criteria and corresponding characteristics of the subject;

comparing of at least one of (i) the neurological disorder of the subject and a neurological disorder of a respective one of the individuals of the second plurality of individuals; and

(ii) the therapeutic agent and a treatment regimen of the respective one of the individuals of the second plurality of individuals; and

analyzing a therapeutic effect of the therapeutic agent based on an observed therapeutic effect of the treatment regimen of the respective one of the individuals of the second plurality of individuals.

7. The method according to claim 6, further comprising generating an updated treatment protocol for the subject based on the analysis of the therapeutic effect, the updated treatment protocol including at least one of (i) a suggested further therapeutic agent, (ii) a suggested combination of therapeutic agents;

and (iii) an adjusted dosage of the therapeutic agent.

8. The method according to claim 1, wherein the data features are extracted from the first set of brain wave data using spectral analysis.

9. A device for assessing a treatment protocol, comprising:

a brain wave detection apparatus gathering a first set of brain wave data corresponding to electrical activity of a brain of a subject; and

a processor extracting from the first set of brain wave data first data features sensitive to a neurological disorder and comparing the first data features to control data to define a baseline profile of brain electropathophysiology and computing one of a first classifying score and a first discriminant score based on the baseline profile to estimate a probability that the baseline profile corresponds to a predetermined pathophysiological condition.

10. The device according to claim 9, wherein the brain wave detection apparatus includes a plurality of EEG electrodes.

11. The device according to claim 9, further comprising a display screen displaying one of the first set of brain wave data, the first data features and the control data.

12. The device according to claim 9, wherein the processor establishes a self norm for the subject based on the first data features prior to administration of a therapeutic agent.

13. The device according to claim 12, wherein the processor compares the self norm to a second set of brain wave data extracted from data gathered after administration of the therapeutic agent to determine an effect of the therapeutic agent.

14. The device according to claim 9, further comprising an interface for coupling the processor to a first database including population brain wave data from each of a plurality of subjects, the processor selecting a portion of the population brain wave data for use as the population norm based on criteria including at least one of age, medical history, gender and ethnicity.

15. The device according to claim 14, wherein none of the plurality of subjects suffers from the neurological disorder.

16. The device according to claim 15, wherein one of the first database and a second database includes further population brain wave data from each of a further plurality of subjects, the processor selecting a portion of the further population brain wave data for use as treatment data corresponding to a suggested treatment protocol based on further criteria including at least one of a neurological disorder of a corresponding subject of the further plurality of subjects, a therapeutic agent administered to the corresponding subject and an observed therapeutic effect of the agent on the corresponding subject.

17. The device according to claim 16, wherein the processor generates the treatment data by analyzing the portion of the further population brain wave data based on a similarity between at least one of (i) the neurological disorder of the subject and the neurological disorder of the corresponding subject;

and (ii) the therapeutic agent administered to the subject and the therapeutic agent administered to the corresponding subject, the processor analyzing a therapeutic effect of the therapeutic agent administered to the subject based on a corresponding therapeutic effect of the therapeutic agent administered to the corresponding subject.

18. The device according to claim 17, wherein the suggested treatment protocol includes at least one of (i) a recommended further therapeutic agent;

(ii) a recommended combination of therapeutic agents; and

(iii) an adjusted dosage of the therapeutic agent.

19. The device according to claim 10, wherein the processor analyzes the first set of brain wave data using at least one of a Fast Fourier Transform (FFT), an Inverse Fast Fourier Transform (IFFT), wavelet analysis, principal component analysis, a logistic regression, a microstate analysis and wavelet demising.

20. A method for formulating a treatment protocol, comprising:

detecting brain wave activity of a subject to generate a first set of brain wave data;

generating a baseline profile by extracting from the first set of brain wave data first selected data features sensitive to a neurological disorder; and

selecting a therapeutic agent based on a comparison of the subject to a plurality of individuals who responded favorably to the therapeutic agent.

21. The method according to claim 20, wherein the individuals include at least one having data features similar to those of the baseline profile and diagnosed with a neurological disorder diagnosed for the subject.

22. The method according to claim 21, wherein the data feature similarity is determined by generating a discriminant score as a function of the standardized score and the individual profiles.

23. The method according to claim 21, wherein the baseline profile is stored in a machine-readable medium and accessed using a unique identifier.