Neuropsychological spatiotemporal pattern recognition

• US 9,135,221 B2
• Filed: 11/26/2012
• Issued: 09/15/2015
• Est. Priority Date: 05/25/2006
• Status: Active Grant

• Current Assignee: ElMindA, Ltd.
• Original Assignee: ElMindA, Ltd.
• Inventors: Shahaf, Goded, Geva, Amir B.
• Primary Examiner(s): Pham, Long
• Application Number:
  US13/684,651
• Publication Number:
  US 20130080127A1
• Time in Patent Office: 1,023 Days
• Field of Search: 382/128, 382/130, 382/132
• US Class Current: 1/1
• CPC Class Codes: A61B 5/245 specially adapted for magnetoencephalographic [MEG] signals
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 18/2323 based on graph theory, e.g. minimum spanning trees [MST] or graph cuts
• CPC Class Codes: G06F 2218/14 by matching peak patterns
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Systems and methods for identifying and analyzing neuropsychological flow patterns, include creating a knowledge base of neuropsychological flow patterns. The knowledge base is formed by obtaining signals from multiple research groups for particular behavioral processes, localizing sources of activity participating in the particular behavioral processes, identifying sets of patterns of brain activity for the behavioral processes and neuropsychologically analyzing the localized sources and the identified patterns for each of the research groups. The neuropsychological analysis includes identifying all possible pathways for the identified sets of patterns, ranking the possible pathways based on likelihood for the particular behavioral process and reducing the number of ranked possible pathways based on additional constraints. A system for comparison of obtained signals from an individual to the created knowledge base is provided. These obtained signals are then used to further update the existing knowledge base.

Claims

1. A method of constructing a flow pattern database, comprising:

obtaining EEG and/or MEG signals from multiple subjects from one or more research groups for a behavioral process;

identifying brain activity patterns for said behavioral process for said research groups using a counting method or method of calculating statistical significances of pairs;

identifying a plurality of candidate pathways for said brain activity patterns;

for each research group, defining a set of flow patterns among functional brain regions based on said candidate pathways, thereby constructing the flow pattern database.

2. The method of claim 1, further comprising, prior to said definition of said set of flow patterns, ranking said candidate pathways based on likelihood for said behavioral process, and reducing the number of candidate pathways based on said ranking.

3. The method of claim 2, being performed iteratively wherein said reducing is based, at least in part, on said flow pattern database.

4. The method of claim 2, wherein said reducing is based, at least in part, on at least one temporal constraint.

5. The method of claim 1, wherein said defining said set of flow patterns comprises using a region matrix.

6. The method of claim 1, further comprising localizing sources of activity participating in said behavioral process, wherein said identification of said plurality of candidate pathways is based also on said sources of activity.

7. The method of claim 1, wherein said obtaining said signals from multiple subjects comprises administering one or more stimuli to said subjects.

8. The method of claim 1, wherein said behavioral process comprises spontaneous activity or a pathological process.

9. The method of claim 1, wherein said signals comprise EEG signals measured using a plurality of EEG electrodes, and the method comprises analyzing waveforms of varying frequencies and amplitudes over time for each EEG electrode.

10. The method of claim 6, wherein said source localization is done prior to said identification of said brain activity patterns.

11. The method of claim 6, wherein said identification of said brain activity patterns is done prior to said source localization.

12. The method of claim 6, being performed iteratively wherein said wherein said source localization is based, at least in part, on said flow pattern database.