SYSTEMS AND METHODS FOR BRAIN ACTIVITY INTERPRETATION

• US 20160235351A1
• Filed: 02/16/2016
• Published: 08/18/2016
• Est. Priority Date: 02/16/2015
• Status: Active Grant

• Current Assignee: Neurosteer Inc.
• Original Assignee: Neurosteer Ltd.
• Inventors: Intrator, Nathan
• Granted Patent: US 9,955,905 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/291 for electroencephalography [EEG]
• 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 5/4076 Diagnosing or monitoring particular conditions of the nervous system A61B5/4821, A61B5/4824 take precedence
• CPC Class Codes: A61B 5/4088 Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• 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 5/726 using Wavelet transforms
• 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: G06F 17/148 Wavelet transforms
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The present invention provides a computer-implemented method, including:

• • a. obtaining, in real-time, by a specifically programmed processor, electrical signal data representative of brain activity of a particular individual;
  • b. processing, in real-time the electrical signal data representative of brain activity of a particular individual based upon a pre-determined predictor associated with a particular brain state, selected from a library of predictors containing a plurality of pre-determined predictors, wherein each individual pre-determined predictor is associated with a unique brain state,
    • wherein the pre-determined predictor associated with a particular brain state includes:
      • i. a pre-determined mother wavelet,
      • ii. a pre-determined representative set of wavelet packet atoms, created from the pre-determined mother wavelet,
      • iii. a pre-determined ordering of wavelet packet atoms, and
      • iv. a pre-determined set of normalization factors,
    • wherein the processing includes:
      • i. causing, by the specifically programmed processor, the electrical signal data to be deconstructed into a plurality of pre-determined deconstructed wavelet packet atoms, utilizing the pre-determined representative set of wavelet packet atoms,
        • wherein time windows of the electrical signal data are projected onto the pre-determined representative set of wavelet packet atoms
        •  wherein the projection is via convolution or inner product, and
        • wherein each pre-determined representative wavelet packet atom corresponds to a particular pre-determined brain activity feature from a library of a plurality of pre-determined brain activity features;
      • ii. storing the plurality of pre-determined deconstructed wavelet packet atoms in at least one computer data object;
      • iii. causing, by the specifically programmed processor, the stored plurality of pre-determined deconstructed wavelet packet atoms to be re-ordered within the computer data object, based on utilizing a pre-determined order;
      • iv. obtaining a statistical measure of the activity of each of the re-ordered plurality of pre-determined deconstructed wavelet packet atoms; and
      • v. normalizing the re-ordered plurality of pre-determined wavelet packet atoms, based on utilizing a pre-determined normalization factor; and
  • c. outputting, a visual indication of at least one personalized mental state of the particular individual, at least one personalized neurological condition of the particular individual, or both, based on the processing,
    • wherein the individual pre-determined predictor associated with a particular brain state from within the plurality of pre-determined predictors is generated by the steps including:
      • i. obtaining the pre-determined representative set of wavelet packet atoms by:
        • a. obtaining from a plurality of individuals, by the specifically programmed processor, at least one plurality of electrical signal data representative of a brain activity of a particular brain state;
        • b. selecting a mother wavelet from a plurality of mother wavelets,
        •  wherein mother wavelet is selected from an wavelet family selected from the group consisting of: Haar, Coiflet Daubehies, and Mayer wavelet families;
        • c. causing, by the specifically programmed processor, the at least one plurality electrical signal data to be deconstructed into a plurality of wavelet packet atoms, using the selected mother wavelet;
        • d. storing the plurality of wavelet packet atoms in at least one computer data object;
        • e. determining, an optimal set of wavelet packet atoms using the pre-determined mother wavelet, and storing the optimal set of wavelet packet atoms in at least one computer data object,
        •  wherein the determining is via utilizing analysis Best Basis algorithm; and
        • f. applying, by the specifically programmed processor, wavelet denoising to the number of wavelet packet atoms in the optimal set;
      • ii. obtaining the pre-determined ordering of wavelet packet atoms by:
        • a. projecting, by the specifically programmed processor, the at least one plurality of electrical signal data representative of a brain activity for each 4 second window of the data onto the pre-determined representative set of wavelet packet atoms;
        • b. storing the projections in at least one computer data object;
        • c. determining, by the specifically programmed processor, the wire length for every data point in the projection by determining the mean absolute distance of the statistical measure of the projections of different channels from their adjacent channels;
        • d. storing the wire length data in at least one computer data object; and
        • e. re-ordering the stored projections, by the specifically programmed computer to minimize a statistical value of the wire length value across each time window, and across all individuals within the plurality of individuals, and across the projections; and
      • iii. obtaining the pre-determined set of normalization factors by:
        • a. determining, by the specifically programmed computer, the mean and standard deviation of the values of the stored projections.

Claims

1. A computer-implemented method, comprising:

a. obtaining, in real-time, by a specifically programmed processor, electrical signal data representative of brain activity of a particular individual;

b. processing, in real-time the electrical signal data representative of brain activity of a particular individual based upon a pre-determined predictor associated with a particular brain state, selected from a library of predictors containing a plurality of pre-determined predictors, wherein each individual pre-determined predictor is associated with a unique brain state,

wherein the pre-determined predictor associated with a particular brain state comprises;

i. a pre-determined mother wavelet,

ii. a pre-determined representative set of wavelet packet atoms, created from the pre-determined mother wavelet,

iii. a pre-determined ordering of wavelet packet atoms, and

iv. a pre-determined set of normalization factors,

wherein the processing comprises;

i. causing, by the specifically programmed processor, the electrical signal data to be deconstructed into a plurality of pre-determined deconstructed wavelet packet atoms, utilizing the pre-determined representative set of wavelet packet atoms,

wherein time windows of the electrical signal data are projected onto the pre-determined representative set of wavelet packet atoms

 

wherein the projection is via convolution or inner product, and

wherein each pre-determined representative wavelet packet atom corresponds to a particular pre-determined brain activity feature from a library of a plurality of pre-determined brain activity features;

ii. storing the plurality of pre-determined deconstructed wavelet packet atoms in at least one computer data object;

iii. causing, by the specifically programmed processor, the stored plurality of pre-determined deconstructed wavelet packet atoms to be re-ordered within the computer data object, based on utilizing a pre-determined order;

iv. obtaining a statistical measure of the activity of each of the re-ordered plurality of pre-determined deconstructed wavelet packet atoms; and

v. normalizing the re-ordered plurality of pre-determined wavelet packet atoms, based on utilizing a pre-determined normalization factor; and

c. outputting, a visual indication of at least one personalized mental state of the particular individual, at least one personalized neurological condition of the particular individual, or both, based on the processing,

wherein an individual pre-determined predictor associated with a particular brain state within the plurality of pre-determined predictors is generated by the steps consisting of;

i. obtaining the pre-determined representative set of wavelet packet atoms by;

a. obtaining from a plurality of individuals, by the specifically programmed processor, at least one plurality of electrical signal data representative of a brain activity of a particular brain state;

b. selecting a mother wavelet from a plurality of mother wavelets,

 

wherein mother wavelet is selected from an wavelet family selected from the group consisting of;

Haar, Coiflet Daubehies, and Mayer wavelet families;

c. causing, by the specifically programmed processor, the at least one plurality electrical signal data to be deconstructed into a plurality of wavelet packet atoms, using the selected mother wavelet;

d. storing the plurality of wavelet packet atoms in at least one computer data object;

e. determining, an optimal set of wavelet packet atoms using the pre-determined mother wavelet, and storing the optimal set of wavelet packet atoms in at least one computer data object,

 

wherein the determining is via utilizing a Best Basis algorithm; and

f. applying, by the specifically programmed processor, wavelet denoising to the number of wavelet packet atoms in the optimal set;

ii. obtaining the pre-determined ordering of wavelet packet atoms by;

a. projecting, by the specifically programmed processor, the at least one plurality of electrical signal data representative of a brain activity for each time window of the data onto the pre-determined representative set of wavelet packet atoms;

b. storing the projections in at least one computer data object;

c. determining, by the specifically programmed processor, the wire length for every data point in the projection by determining the mean absolute distance of the statistical measure of the projections of different channels from their adjacent channels;

d. storing the wire length data in at least one computer data object; and

e. re-ordering the stored projections, by the specifically programmed computer to minimize a statistical value of the wire length value across each time window, and across all individuals within the plurality of individuals, and across the projections; and

;

iii. obtaining the pre-determined set of normalization factors by;

a. determining, by the specifically programmed computer, the mean and standard deviation of the values of the stored projections.

2. The computer implemented method of claim 1, wherein each time window is a four second time window.

3. The computer implemented method of claim 1, wherein the statistical value for the re-ordering is selected from the group consisting of:

the mean of the sum of the absolute differences of the wavelet packet atom, and a mean of the sum of (1−

correlation) of the wavelet packet atoms.

4. The computer-implemented method of claim 1, wherein the electrical signal data representative of brain activity of a particular individual is recorded using two electrodes located on the forehead of the particular individual.

5. The computer-implemented method of claim 1, wherein the visual indication of at least one personalized mental state of the particular individual is used to identify an underlying mental state, an underlying neurological condition, or a combination of an underlying mental state and an underlying neurological condition, in the particular individual, wherein the specifically programmed computer utilizes at least one machine learning algorithm selected from the group consisting of logistic regression modeling, support vector machine modeling, and a deep learning modeling, to assign at least one specific brain state to the visual indication of at least one personalized mental state of the particular individual, wherein the at least one specific brain state is associated with a mental state, a neurological condition, or a combination of a mental state and a neurological condition.

6. The computer-implemented method of claim 5, wherein the assignment of at least one specific brain state to the visual indication of at least one personalized mental state of the particular individual identifies an abnormality in at least one neural network in the brain of the particular individual associated with a particular neurological condition.

7. The computer-implemented method of claim 6, wherein the abnormality in at least one neural network in the brain of the particular individual is used to diagnose the particular individual having a neurological condition.

8. The computer-implemented method of claim 6, wherein the neurological condition is selected from the group consisting of, Alzheimer'"'"'s disease, dementia, stress, fatigue, anxiety, epilepsy, traumatic brain injury, loss of cognitive function, coma, a lack of response, or inappropriate response to external stimuli associated with autism, or autism spectrum disorders, a lack of concentration, chronic pain, migraine, and sleep disorders.

9. The computer-implemented method of claim 5, wherein the at least one specific brain state is used to determine the emotional state of the particular individual.

10. The computer-implemented method of claim 1, wherein the particular individual is receiving a therapy, and the visual indication of at least one personalized mental state of the particular individual is used to determine the effectiveness of the therapy.

11. The computer-implemented method of claim 1, wherein the particular individual is receiving a therapy, and the visual indication of at least one personalized mental state of the particular individual is used to determine the nature of the therapy to be administered.

12. The computer-implemented method of claim 1, wherein the particular individual is receiving a therapy, and the visual indication of at least one personalized mental state of the particular individual is used to determine the duration of the therapy.

13. The computer-implemented method of claim 1, wherein the particular individual is receiving a therapy, and the visual indication of at least one personalized mental state of the particular individual is used to determine the dosing regimen of the therapy.

14. The computer-implemented method of claim 10, wherein the therapy is an anesthetic agent, and the effectiveness of the anesthetic is determined by the particular individual'"'"'s ability to feel pain and/or the individual'"'"'s perceived pain level and the correlation to the change in the visual indication of at least one personalized mental state of the particular individual.

15. The computer-implemented method of claim 10, wherein the therapy is a migraine therapy, and the effectiveness of the migraine therapy is determined by the particular individual'"'"'s ability to feel pain, and/or the individual'"'"'s perceived pain level and the correlation to the change in the visual indication of at least one personalized mental state of the particular individual.

16. The computer implemented method of claim 1, wherein the particular individual is performing a specific cognitive task.

17. The computer implemented method of claim 16, wherein the specific cognitive task is selected from the group consisting of short and/or long term memory recall, e-learning, meditation, and concentration.

18. The computer implemented method of claim 1, wherein the particular individual has a particular brain state at a certain time.

19. A specifically programmed computer system comprising:

a. at least one specialized computer machine comprising;

i. a non-transient memory, electronically storing particular computer executable program code; and

ii. at least one computer processor which, when executing the particular program code, becomes a specifically programmed computer processor configured to perform at least the following operations;

1. obtaining, in real-time, by a specifically programmed processor, electrical signal data representative of brain activity of a particular individual;

2. processing, in real-time the electrical signal data representative of brain activity of a particular individual based upon an individual pre-determined predictor associated with a particular brain state, selected from a library of predictors containing a plurality of pre-determined predictors, wherein each individual pre-determined predictor is associated with a unique brain state,

wherein the pre-determined predictor associated with a particular brain state comprises;

 

i. a pre-determined mother wavelet,

 

ii. a pre-determined representative set of wavelet packet atoms created from the pre-determined mother wavelet,

 

iii. a pre-determined ordering of wavelet packet atoms, and

 

iv. a pre-determined set of normalization factors,

wherein the processing comprises;

 

i. causing, by the specifically programmed processor, the electrical signal data to be deconstructed into a plurality of pre-determined deconstructed wavelet packet atoms, utilizing the pre-determined representative set of wavelet packet atoms,

 

wherein time windows of the electrical signal data are projected onto the pre-determined representative set of wavelet packet atoms

 

wherein the projection is via convolution or inner product, and

 

wherein each pre-determined representative wavelet packet atom corresponds to a particular pre-determined brain activity feature from a library of a plurality of pre-determined brain activity features;

 

ii. storing the plurality of pre-determined deconstructed wavelet packet atoms in at least one computer data object;

 

iii. causing, by the specifically programmed processor, the stored plurality of pre-determined deconstructed wavelet packet atoms to be re-ordered within the computer data object, based on utilizing a pre-determined order;

 

iv. obtaining a statistical measure of the activity of each of the re-ordered plurality of pre-determined deconstructed wavelet packet atoms; and

 

v. normalizing the re-ordered plurality of pre-determined wavelet packet atoms, based on utilizing a pre-determined normalization factor; and

3. outputting, a visual indication of at least one personalized mental state of the particular individual, at least one personalized neurological condition of the particular individual, or both, based on the processing,

wherein an individual pre-determined predictor associated with a particular brain state within the plurality of pre-determined predictors is generated by the steps consisting of;

 

i. obtaining the pre-determined representative set of wavelet packet atoms by;

 

1. obtaining from a plurality of individuals, by the specifically programmed processor, at least one plurality of electrical signal data representative of a brain activity of a particular brain state;

 

2. selecting a mother wavelet from a plurality of mother wavelets,

 

wherein mother wavelet is selected from an wavelet family selected from the group consisting of;

Haar, Coiflet Daubehies, and Mayer wavelet families;

 

3. causing, by the specifically programmed processor, the at least one plurality electrical signal data to be deconstructed into a plurality of wavelet packet atoms, using the selected mother wavelet;

 

4. storing the plurality of wavelet packet atoms in at least one computer data object;

 

5. determining, an optimal set of wavelet packet atoms, and storing the optimal set of wavelet packet atoms in at least one computer data object,

 

wherein the determining is via utilizing a Best Basis algorithm; and

 

6. applying, by the specifically programmed processor, wavelet denoising to the number of wavelet packet atoms in the optimal set;

 

ii. obtaining the pre-determined ordering of wavelet packet atoms by;

 

1. projecting, by the specifically programmed processor, the at least one plurality of electrical signal data representative of a brain activity for time window of the data onto the pre-determined representative set of wavelet packet atoms;

 

2. storing the projections in at least one computer data object;

 

3. determining, by the specifically programmed processor, the wire length for every data point in the projection by determining the mean absolute distance of the statistical measure of the projections of different channels from their adjacent channels;

 

4. storing the wire length data in at least one computer data object; and

 

5. re-ordering the stored projections, by the specifically programmed computer to minimize a statistical value of the wire length value across each time window, and across all individuals within the plurality of individuals, and across the projections; and

 

iii. obtaining the pre-determined set of normalization factors by;

 

1. determining, by the specifically programmed computer, the mean and standard deviation of the values of the stored projections.

20. The system of claim 19, wherein each time window is a four second time window.

21. The system of claim 19, wherein the statistical value is selected from the group consisting of:

the mean of the sum of the absolute differences of the wavelet packet atoms, and a mean of the sum of (1−

correlation) of the wavelet packet atoms.

22. The system of claim 19, wherein the electrical signal data representative of brain activity of a particular individual is recorded using two electrodes located on the forehead of the particular individual.

23. The system of claim 19, wherein the visual indication of at least one personalized mental state of the particular individual is used to identify an underlying mental state, an underlying neurological condition, or a combination of an underlying mental state and an underlying neurological condition, in the particular individual, wherein the specifically programmed computer utilizes at least one machine learning algorithm selected from the group consisting of logistic regression modeling, support vector machine modeling, and a deep learning modeling, to assign at least one specific brain state to the visual indication of at least one personalized mental state of the particular individual, wherein the at least one specific brain state is associated with a mental state, a neurological condition, or a combination of a mental state and a neurological condition.

24. The system of claim 23, wherein the assignment of at least one specific brain state to the visual indication of at least one personalized mental state of the particular individual identifies an abnormality in at least one neural network in the brain of the particular individual associated with a particular neurological condition.

25. The system of claim 24, wherein the abnormality in at least one neural network in the brain of the particular individual is used to diagnose the particular individual having a neurological condition.