Method and system for managing pain

• US 9,713,433 B2
• Filed: 11/11/2014
• Issued: 07/25/2017
• Est. Priority Date: 11/13/2013
• Status: Active Grant

• Current Assignee: ElMindA, Ltd.
• Original Assignee: ElMindA, Ltd.
• Inventors: Reches, Amit, Gadot, Ronen, Geva, Amir B., Stern, Yaki
• Primary Examiner(s): BERHANU, ETSUB D
• Application Number:
  US14/770,099
• Publication Number:
  US 20160213276A1
• Time in Patent Office: 987 Days
• Field of Search: 600544, 600545
• 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 5/4064 Evaluating the brain A61B5/031, A61B5/369, A61B5/14553 take precedence
• CPC Class Codes: A61B 5/4343 Pregnancy and labour monitoring, e.g. for labour onset detection
• CPC Class Codes: A61B 5/4824 Touch or pain perception evaluation A61B5/4821 takes precedence
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• 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
• CPC Class Codes: A61N 1/36021 for treatment of pain
• CPC Class Codes: A61N 1/36071 Pain
• CPC Class Codes: A61N 1/36139 with automatic adjustment
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method of managing pain using neurophysiological data acquired from the brain of a subject is disclosed. The method comprises: identifying activity-related features in the data; parceling the data according to the activity-related features to define a plurality of capsules, each representing a spatiotemporal activity region in the brain; comparing at least some of the defined capsules to at least one reference capsule; and assessing the likelihood that the subject is experiencing pain responsively to the comparison.

Claims

1. A method of managing labor pain using neurophysiological data acquired from the brain of a female subject, the method comprising:

identifying activity-related features in said data;

constructing a subject-specific brain network activity (BNA) pattern having a plurality of nodes, wherein each node represents a feature of said activity-related features, and each pair of nodes is assigned with a connectivity weight;

calculating a BNA pattern similarity describing a comparison between said constructed BNA pattern and a baseline BNA pattern;

parceling the data according to said activity-related features to define a plurality of capsules, each representing a spatiotemporal activity region in the brain;

comparing at least some of said defined capsules to at least one reference capsule; and

assessing the likelihood that the subject is experiencing labor pain responsively to said BNA pattern similarity and said comparison between said defined capsules and said at least one reference capsule;

wherein said neurophysiological data comprises data acquired before, during and/or after a treatment, and the method comprises applying said treatment.

2. The method according to claim 1, wherein said baseline BNA pattern corresponds to neurophysiological data acquired from the same subject at a different time.

3. The method according to claim 1, wherein said baseline BNA pattern is based on a group BNA pattern characterizing a group of subjects indentified as not experiencing labor pain.

4. The method according to claim 1, wherein said baseline BNA pattern is based on a group BNA pattern characterizing a group of subjects indentified as experiencing labor pain.

5. The method according to claim 1, further comprising repeating said construction of subject-specific BNA and said calculation of BNA pattern similarity at least once.

6. The method according to claim 1, further comprising repeating said construction of subject-specific BNA and said calculation of BNA pattern similarity at least once, wherein each subject-specific BNA is constructed based on different reference data or a different model but using the same neurophysiological data of the subject, wherein each subject-specific BNA is compared to a baseline BNA pattern being associated with a different brain condition, and wherein said assessment is responsive to at least two BNA pattern similarities.

7. The method according to claim 6, wherein at least one baseline BNA pattern characterizes a group of subjects indentified as not experiencing labor pain, and at least one baseline BNA pattern characterizes a group of subjects indentified as experiencing labor pain.

8. The method according to claim 1, further comprising determining a pain index based on at least said BNA pattern similarity.

9. The method according to claim 1, further comprising constructing several subject-specific BNA patterns corresponding to different time intervals, and displaying said subject-specific BNA patterns on a time axis.

10. The method according to claim 1, further comprising assessing the effect of said treatment by comparing a BNA pattern similarity calculated using at least said baseline BNA pattern and a subject-specific BNA pattern constructed based on data acquired before a treatment, to a BNA pattern similarity calculated using at least said baseline BNA pattern and a subject-specific BNA pattern constructed based on data acquired after a treatment.

11. The method according to claim 1, further comprising assessing the effect of said treatment by comparing a subject-specific BNA pattern corresponding to data acquired before a treatment to a subject-specific BNA pattern corresponding to data acquired during and/or after a treatment.

12. The method according to claim 1, wherein said treatment comprises a pharmacological treatment employing an active agent.

13. The method according to claim 1, wherein said treatment comprises local stimulation to the brain.

14. The method according to claim 13, wherein said local stimulation is at one or more locations corresponding to a spatial location of at least one of said nodes.

15. The method according to claim 13, wherein said assessment of said likelihood is executed repeatedly, and the method comprises varying said local stimulation responsively to variations in said likelihood.

16. The method according to claim 1, further comprising, prior to said calculation of said BNA pattern similarity, applying a feature selection procedure to said subject-specific BNA pattern to provide at least one BNA sub-pattern, wherein said calculation of said BNA pattern similarity is executed separately for each of said at least one BNA sub-pattern.

17. The method according to claim 1, wherein said capsule comparison comprises calculating, for each of said at least some of said defined capsules, a statistical score of a spatiotemporal vector corresponding to said capsule using multidimensional statistical distribution describing a respective database capsule.

18. The method according to claim 17, wherein each entry of said database is also associated with a weight, and the method further comprises weighing said statistical score using said weight.

19. The method according to claim 1, herein said capsule comparison is executed irrespective of any inter-capsule relation.

20. The method according to claim 19, wherein said inter-capsule relation comprises at least one of spatial proximity between two defined capsules, temporal proximity between two defined capsules, spectral proximity between two defined capsules, and energetic proximity between two defined capsules.

21. The method according to claim 1, further comprising determining inter-capsule relations among said capsules, and constructing a capsule network pattern responsively to said inter-capsule relations, wherein a database capsule comprises database capsule network patterns, and where an inter-capsule comparison comprises comparing said constructed pattern to said database patterns.

22. The method according to claim 1, wherein said at least one reference capsule comprises an annotated database capsule stored in a database having a plurality of entries, and the method further comprises accessing said database.

23. The method according to claim 1, wherein said at least one reference capsule comprises a baseline capsule defined using neurophysiological data acquired from the same subject at a different time.

24. The method according to claim 23, further comprising comparing a variation of a particular capsule relative to said baseline capsule, to a previously stored variation of a first capsule annotated as normal and a second capsule also annotated as normal.

25. The method according to claim 1, wherein said at least one reference capsule comprises a capsule defined using neurophysiological data acquired from a different subject.

26. The method according to claim 1, further comprising, prior to said capsule comparison, applying a feature selection procedure to said plurality of capsules to provide at least one sub-set of capsules, wherein said comparison is executed separately for each of said at least one sub-set of capsules.

27. A system for managing labor pain using neurophysiological data, comprising:

a data processor configured for receiving the neurophysiological data, and executing the method according to claim 1; and

a controller connectable to a treatment system and configured for controlling said treatment system to apply said treatment.