Method for assessing brain function and portable automatic brain function assessment apparatus

• US 7,904,144 B2
• Filed: 08/02/2005
• Issued: 03/08/2011
• Est. Priority Date: 08/02/2005
• Status: Active Grant

• Current Assignee: Brainscope SPV LLC
• Original Assignee: Brainscope Company Inc.
• Inventors: Causevic, Elvir, Combs, Arthur H.
• Primary Examiner(s): Nasser; Robert L
• Application Number:
  US11/195,001
• Publication Number:
  US 20070032737A1
• Time in Patent Office: 2,044 Days
• Field of Search: None
• US Class Current: 600/544
• 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 5/377 using evoked responses
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• 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: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method and apparatus for performing rapid brain assessment may provide emergency triage to head trauma patients by analyzing a combination of spontaneous and evoked brain potentials. The spontaneous and evoked potentials are analyzed, and the results classified, to present a real-time assessment of a patient'"'"'s brain, diagnosing any potential abnormalities therein.

Claims

1. A method of determining a neurological state of a subject comprising the steps of:

acquiring spontaneous and evoked signals through an electrode set;

de-noising the acquired signals;

extracting features from the de-noised signals using wavelet packet analysis; and

classifying the extracted features.

2. The method according to claim 1, wherein

the electrode set comprises less than 19 electrodes.

3. The method according to claim 1, wherein the spontaneous signals comprise:

electroencephalographic (EEG) signals.

4. The method according to claim 1, wherein the evoked signals comprise:

auditory evoked potentials (AEP).

5. The method according to claim 4, wherein the auditory evoked potentials comprise at least one of:

auditory brainstem response (ABR) potentials, auditory mid-latency response (AMLR) potentials, or auditory late response (ALR) potentials, including P100 responses and P300 responses.

6. The method according to claim 1, wherein the step of de-noising the signals comprises:

de-noising the acquired signals using signal transformation and non-linear processing.

7. The method according to claim 6, wherein the step of de-noising the signals further comprises:

filtering the signals using a dual-tree complex wavelet transform of the acquired signals.

8. The method of according to claim 1, wherein the step of de-noising the signals comprises:

removing artifacts from the acquired signal by identifying artifact sections in the acquired signals and subtracting the identified artifact sections.

9. The method according to claim 1, wherein the step of extracting features comprises:

performing spectral analysis and statistical procedures on the de-noised signals, and extracting features.

10. The method according to claim 9, wherein applying spectral analysis comprises:

applying a Fast Fourier Transform (FFT) to the de-noised signals, dividing the signals into frequency bands.

11. The method according to claim 1, wherein

the extracted features include absolute and relative power, symmetry and coherence.

12. The method according to claim 1, wherein the step of extracting features further comprises:

applying Fast Fourier Transform (FFT) analysis on the de-noised signals to extract features.

13. The method according to claim 1, wherein the step of classifying comprises:

applying discriminant analysis to the extracted features to classify the extracted features into one or more diagnostic categories.

14. The method according to claim 1, further comprising the step of:

determining a neurological state of the subject based on the classification.

15. The method according to claim 14, wherein the step of determining further comprises:

determining if the neurological state is normal or abnormal.

16. The method according to claim 15, further comprising:

determining if an abnormal neurological state is psychiatric or “

functional”

in nature, organic in nature, or an emergency or “

Alert”

condition.

17. The method according to claim 16, wherein neurological states that constitute an emergency condition include seizure, abnormal brainstem response, or burst suppression.

18. The method according to claim 15, further comprising:

determining if an abnormal neurological state that is organic in nature is global or lateral.

19. The method according to claim 1, further comprising:

graphically displaying the determination;

graphically displaying representations of the brain based on the determination;

storing data related to the determination.

20. The method according to claim 1, wherein the step of extracting features comprises:

performing a high-speed quantitative electroencephalograph (qEEG) algorithm on the de-noised signals to extract features.

21. A method for providing a triage assessment of a patient'"'"'s brain function comprising the steps of:

measuring, using electrodes, the spontaneous brain activity of the patient;

stimulating the patient and measuring the evoked brain activity therefrom;

processing, using a processor coupled to a memory having instructions for processing stored therein, the spontaneous and evoked brain activity, wherein

the processing is performed in real-time using a wavelet-packet algorithm to extract signal features;

providing a triage assessment of the patient based on the processed brain activity.

22. The method according to claim 21, wherein

the steps of processing and providing an assessment are performed by a portable hand-held device.

23. A method of determining a neurological state of a subject comprising the steps of:

acquiring spontaneous and evoked signals through an electrode set;

processing the spontaneous and evoked signals using a processor coupled to a memory having instructions for processing stored therein, wherein processing the spontaneous and evoked signals further comprises the steps of;

de-noising the acquired signals;

extracting features from the de-noised signals;

classifying the extracted features; and

determining a neurological state of the subject based on the classification, wherein determining the neurological state further comprises the steps of;

determining if the neurological state is normal or abnormal; and

determining if an abnormal neurological state that is organic in nature is global or lateral.

24. The method according to claim 23, wherein the spontaneous signals comprise electroencephalographic (EEG) signals and the evoked signals comprise auditory evoked potentials (AEP).

25. The method according to claim 23, wherein the step of extracting features from the denoised signal comprises using a wavelet packet transform algorithm.

26. The method of claim 23, wherein said classifying step further comprises non-linear classification.

27. A method of determining a neurological state of a subject comprising the steps of:

acquiring brain electrical signals using a set of neurological electrodes;

processing the acquired brain electrical signals using a processor coupled to a memory having instructions for processing stored therein, wherein processing the acquired brain electrical signals further comprises the steps of;

de-noising the acquired signals;

extracting features from the de-noised signals;

classifying the extracted features into a first set of diagnostic categories using discriminant analysis, wherein the first set of diagnostic categories are indicative of whether the subject has a normal or an abnormal neurological state; and

classifying an abnormal brain state into a second set of diagnostic categories using discriminant analysis, wherein the second set of diagnostic categories are indicative of whether the subject'"'"'s abnormal neurological state is psychiatric or “

functional”

in nature, organic in nature, or an emergency or “

Alert”

condition.

28. The method according to claim 27, wherein an emergency or “

Alert”

condition is further classified into diagnostic categories indicative of seizure, abnormal brainstem response, or burst suppression.

29. The method according to claim 27, wherein an organic condition is further classified into diagnostic categories indicative of a global or lateral abnormality.

30. The method according to claim 27, wherein electrical brain signals comprise spontaneous brain signals.

31. The method according to claim 27, wherein the electrical brain signals comprise evoked potentials.

32. The method according to claim 27, wherein the electrical brain signals comprise spontaneous brain signals and evoked potentials.

33. The method according to claim 27, wherein the extracted features comprise quantitative non-linear features.

34. The method of claim 27, wherein said classifying step further comprises non-linear classification.