Systems and methods for analyzing and assessing depression and other mood disorders using electroencephalographic (EEG) measurements

• US 8,311,622 B2
• Filed: 11/30/2006
• Issued: 11/13/2012
• Est. Priority Date: 12/01/2005
• Status: Expired due to Fees

• Current Assignee: NEBA Health, LLC
• Original Assignee: NEBA Health, LLC
• Inventors: Snyder, Steven M., Falk, James D.
• Primary Examiner(s): Kahelin, Michael
• Assistant Examiner(s): WEARE, MEREDITH H
• Application Number:
  US11/565,305
• Publication Number:
  US 20070135728A1
• Time in Patent Office: 2,175 Days
• Field of Search: 600/544, 600/545
• 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 computer operable method for analyzing and assessing a mood disorder in a person, comprising:

receiving, in the computer, a plurality of data sets of electroencephalography data associated with the person, each data set comprising a plurality of epochs, the plurality of data sets including at least one static component corresponding to a baseline level of behavioral functioning of the person and including at least one dynamic component corresponding to an acute level of behavioral expression of the person, the plurality of data sets representing similar collection conditions for the person;

determining, by operation of the computer, the static component of a portion of the plurality of data sets, wherein the portion comprises data from each of multiple epochs of the plurality of data sets, wherein the static component is independent of the dynamic component included in the plurality of data sets, wherein the static component is determined by computing the intersection of all spectral patterns of the portion;

determining, by operation of the computer, the dynamic component of the portion of the plurality of data sets, wherein the dynamic component is independent of the static component included in the plurality of data sets, wherein the dynamic component is determined by computing the intersection of all spectral patterns of the portion;

determining, by operation of the computer a static asymmetry in the static component, wherein the static asymmetry is determined by computing an intersection between a left spectral pattern of the static component and a right spectral pattern of the static component and by removing the intersection from the left and right spectral patterns of the static component;

determining, by operation of the computer, a dynamic asymmetry in the dynamic component, wherein the dynamic asymmetry is determined by computing an intersection between a left spectral pattern of the dynamic component and a right spectral pattern of the dynamic component and by removing the intersection from the left and right spectral patterns of the dynamic component; and

based at least in part on the static asymmetry in the static component and the dynamic asymmetry in the dynamic component, determining, by operation of the computer, an indication for whether the person is at risk for the mood disorder,

wherein the mood disorder comprises at least one of the following;

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

2. The method of claim 1, further comprising:

based at least in part on the dynamic asymmetry in the dynamic component, determining, by operation of the computer, an indication for predicting and evaluating a treatment response of the mood disorder,

wherein the plurality of data sets comprises a plurality of data sets representing similar collection conditions for the person collected pre-treatment and comprises a plurality of data sets representing similar collection conditions for the person collected post-treatment.

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

collecting, in the computer, electroencephalography data from the person wherein the electroencephalography data comprises a plurality of data sets and wherein each data set comprises a plurality of epochs, the plurality of data sets including at least one static component corresponding to a baseline level of behavioral functioning of the person and including at least one dynamic component corresponding to an acute level of behavioral expression of the person, the plurality of data sets representing similar collection conditions for the person;

determining, by operation of the computer, the static component associated with a portion of the plurality of data sets, wherein the portion comprises data from each of multiple epochs of the plurality of data sets, wherein the static component is determined by computing the intersection of all spectral patterns of the portion;

determining, by operation of the computer, the dynamic component of the portion of the plurality of data sets wherein the dynamic component is independent of the static component included in the plurality of data sets, wherein the dynamic component is determined by computing the intersection of all spectral patterns of the portion;

determining, by operation of the computer, a static asymmetry in the static component and a dynamic asymmetry in the dynamic component, wherein a static asymmetry is determined by computing an intersection between a left spectral pattern of the static component and a right spectral pattern of the static component and by removing the intersection from the left and right spectral patterns of the static component, wherein a dynamic asymmetry is determined by computing an intersection between a left spectral pattern of the dynamic component and a right spectral pattern of the dynamic component and by removing the intersection from the left and right spectral patterns of the dynamic component;

based at least in part on the static asymmetry in the static component and the dynamic asymmetry in the dynamic component, evaluating, by operation of the computer, a characteristic associated with the mood disorder,

wherein the characteristic comprises at least one of the following;

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

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

a data collection module adapted to;

receive a plurality of data sets of electroencephalography data associated with the person, each data set comprising a plurality of epochs, the plurality of data sets including at least one static component corresponding to a baseline level of behavioral functioning of the person and including at least one dynamic component corresponding to an acute level of behavioral expression of the person, the plurality of data sets representing similar collection conditions for the person;

a report generation module adapted to;

determine the static component of a portion of the plurality of data sets, wherein the portion comprises data from each of multiple epochs of the plurality of data sets, wherein the static component is independent of the dynamic component included in the plurality of data sets, wherein the static component is determined based on by computing the intersection of all spectral patterns of the portion;

determine the dynamic component of the portion, wherein the dynamic component is independent of the static component included in the plurality of data sets, wherein the dynamic component is determined by computing the intersection of all spectral patterns of the portion;

determine static asymmetry in the static component, wherein the static asymmetry is determined by computing an intersection between a left spectral pattern of the static component and a right spectral pattern of the static component and by removing the intersection from the left and right spectral patterns of the static component;

determine dynamic asymmetry in the dynamic component, wherein the dynamic asymmetry is determined by computing an intersection between a left spectral pattern of the dynamic component and a right spectral pattern of the dynamic component and by removing the intersection from the left and right spectral patterns of the dynamic component; and

based at least in part on the static asymmetry in the static component and the dynamic asymmetry in the dynamic component, output an indication of whether the person is at risk for the mood disorder,

wherein the mood disorder comprises at least one of the following;

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

5. The system of claim 4, wherein the report generation module is further adapted to:

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

wherein the plurality of data sets comprises a plurality of data sets representing similar collection conditions for the person collected pre-treatment and comprises a plurality of data sets representing similar collection conditions for the person collected post-treatment.

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

a data collection module adapted to;

receive a plurality of data sets of electroencephalography data associated with the person, each data set comprising a plurality of epochs, the plurality of data sets including at least one static component corresponding to a baseline level of behavioral functioning of the person and including at least one dynamic component corresponding to an acute level of behavioral expression of the person, the plurality of data sets representing similar collection conditions for the person;

a report generation module adapted to;

determine the static component of a portion of the plurality of data sets, wherein the portion comprises data from each of multiple epochs of the plurality of data sets, wherein the static component is independent of the dynamic component included in the plurality of data sets, wherein the static component is determined by computing the intersection of all spectral patterns of the portion;

determine the dynamic component of the portion, wherein the dynamic component is independent of the static component included in the plurality of data sets, wherein the dynamic component is determined by computing the intersection of all spectral patterns of the portion;

determine static asymmetry in the static component, wherein the static asymmetry is determined by computing an intersection between a left spectral pattern of the static component and a right spectral pattern of the static component and by removing the intersection from the left and right spectral patterns of the static component;

determine dynamic asymmetry in the dynamic component, wherein the dynamic asymmetry is determined by computing an intersection between a left spectral pattern of the dynamic component and a right spectral pattern of the dynamic component and by removing the intersection from the left and right spectral patterns of the dynamic component; and

based at least in part on the static asymmetry in the static component and the dynamic asymmetry in the dynamic component, output an indication of a characteristic of the mood disorder,

wherein the characteristic comprises at least one of the following;

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