Systems and Methods for Analyzing and Assessing Depression and Other Mood Disorders Using Electroencephalographic (EEG) Measurements

• US 20070135728A1
• Filed: 11/30/2006
• Published: 06/14/2007
• Est. Priority Date: 12/01/2005
• Status: Active Grant

• Current Assignee: NEBA Health, LLC
• Original Assignee: Lexicor Medical Technology LLC
• Inventors: Snyder, Steven, Falk, James
• Granted Patent: US 8,311,622 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/7257 using Fourier transforms

Abstract

This invention is directed to systems and methods for analyzing depression, and more particularly relates to systems and methods for analyzing and assessing depression and mood disorders in an individual using electroencephalographic measurements. Embodiments of the invention are not limited to depression, but can also include other mood disorders such as bipolar disorder and other disorders with at least one genetic-related component.

Claims

1. A method for analyzing and assessing a mood disorder in a person, comprising:

receiving a plurality of electroencephalography data associated with the person;

determining at least one static component of a portion of the plurality of electroencephalography data;

determining static asymmetry in the static component of the portion of the plurality of electroencephalography data; and

based at least in part on the static asymmetry in the static component of the portions of plurality of electroencephalography data, determining an indication for whether the person is at risk for the mood disorder.

2. The method of claim 1, further comprising:

determining at least one dynamic component of a portion of the plurality of electroencephalography data;

determining dynamic asymmetry in the dynamic component of the portion of the plurality of electroencephalography data based at least in part on the dynamic asymmetry in the dynamic component of the portions of the electroencephalography data, determining an indication for predicting and evaluating a treatment response of the mood disorder.

3. The method of claim 1, wherein determining at least one static component of the portion of the plurality of electroencephalography data comprises determining a static spectral pattern.

4. The method of claim 2, wherein determining dynamic asymmetry in the dynamic component of the portion of the plurality of the electroencephalography data comprises determining a dynamic spectral pattern.

5. The method of claim 3, wherein determining static asymmetry in the static component of the portion of the plurality of the electroencephalography data comprises removing the intersection of a left and right spectral pattern from an original left and right static spectral pattern.

6. The method of claim 4, wherein determining dynamic asymmetry in the dynamic component of the portion of the plurality of the electroencephalography data comprises removing the intersection of a left and right dynamic spectral pattern from an original left and right dynamic spectral pattern.

7. The method of claim 1, wherein determining static asymmetry in the static component of the portion of the plurality of the electroencephalography data can further comprise:

determining an average of maximum and minimum powers of a right and left side static component.

8. The method of claim 2, wherein determining dynamic asymmetry in the dynamic component of the electroencephalography data can further comprise:

determining an average of maximum and minimum powers of a right and left side dynamic component.

9. The method of claim 1, wherein the mood disorder comprises at least one of the following:

depression, bipolar disorder, or a disorder with at least one genetic-related component.

10. A method for analyzing and assessing a mood disorder in person using electroencephalography data, comprising:

collecting electroencephalography data from the person;

determining a static component associated with at least some of the electroencephalography data;

determining a dynamic component associated with at least some of the electroencephalography data;

determining asymmetry in either the static or the dynamic component;

based at least in part on either the asymmetry in the static component or the dynamic component, evaluating a characteristic associated with the mood disorder.

11. The method of claim 10, wherein determining asymmetry in either the static or the dynamic component comprises:

based at least in part on the electroencephalography data, determining a left side spectral pattern;

based at least in part on the electroencephalography data, determining a right side spectral pattern; and

removing an intersecting portion of the left side spectral pattern and right side spectral pattern to obtain an overall asymmetric spectral pattern.

12. The method of claim 11, wherein determining asymmetry in either the static or the dynamic component further comprises:

evaluating a ratio of the intersecting portion of the left side spectral pattern and right side spectral pattern with a union of the left side spectral pattern and right side spectral pattern.

13. The method of claim 11, wherein determining asymmetry in either the static or the dynamic component comprises:

implementing a learning-type algorithm to define one or more weighting factors to ascertain a similarity of each frequency band associated with the electroencephalography data.

14. The method of claim 11, wherein determining asymmetry in either the static or the dynamic component comprises:

determining a percent of time the patient'"'"'s left side is favored or disfavored relative to the patient'"'"'s right side; and

comparing the percent of time the patient'"'"'s left side is favored or disfavored relative to the patient'"'"'s right side.

15. The method of claim 11 wherein determining asymmetry in either the static or the dynamic component comprises:

using at least one vector to derive a respective power for each frontal region; and

comparing the respective powers for each frontal region.

16. The method of claim 10, wherein the characteristic can comprise at least one of the following:

a risk of having the mood disorder, or a symptom of the mood disorder.

17. A method for analyzing and assessing a mood disorder in person using electroencephalography data, comprising:

collecting electroencephalography data from the person;

determining a static component associated with at least some of the electroencephalography data;

determining asymmetry in the static component;

based at least in part on the asymmetry in the static component, evaluating a characteristic associated with the mood disorder.

18. A method for analyzing and assessing a mood disorder in person using electroencephalography data, comprising:

collecting electroencephalography data from the person;

determining a dynamic component associated with at least some of the electroencephalography data;

determining asymmetry in the dynamic component;

based at least in part on the asymmetry in the dynamic component, evaluating a characteristic associated with the mood disorder.

19. A system for analyzing and assessing a mood disorder in a person, comprising:

a data collection module adapted to;

receive a plurality of electroencephalography data associated with the person;

a report generation module adapted to;

determine at least one static component of a portion of the plurality of electroencephalography data;

determine static asymmetry in the static component of the portion of the plurality of electroencephalography data; and

based at least in part on the static asymmetry in the static component of the portions of plurality of electroencephalography data, output an indication of whether the person is at risk for the mood disorder.

20. The system of claim 19, wherein the report generation module is further adapted to:

determine at least one dynamic component of a portion of the plurality of electroencephalography data;

determine dynamic asymmetry in the dynamic component of the portion of the plurality of electroencephalography data; and

based at least in part on the dynamic asymmetry in the dynamic component of the portions of the electroencephalography data, output an indication of predicting a treatment response of the mood disorder; and

based at least in part on the dynamic asymmetry in the dynamic component of the portions of the electroencephalography data, output an indication of evaluating a treatment of the mood disorder.