Method and Apparatus For Assessing Brain Function Using Diffusion Geometric Analysis

• US 20090264785A1
• Filed: 04/18/2008
• Published: 10/22/2009
• Est. Priority Date: 04/18/2008
• Status: Abandoned Application

• Current Assignee: Brainscope SPV LLC
• Original Assignee: Brainscope Company Inc.
• Inventors: Coifman, Ronald, Warner, Frederick, Causevic, Elvir, Coppi, Andreas
• Application Number:
  US12/105,439
• Publication Number:
  US 20090264785A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• 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 5/7203 for noise prevention, reduction or removal
• 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 2218/06 by applying a scale-space analysis, e.g. using wavelet analysis
• CPC Class Codes: G06F 2218/08 Feature extraction
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method of extracting features and classifying a neurological state of a subject is provided. The method includes recording brain electrical activity, organizing the recorded data set into digital documents, computing a diffusion geometry on the data set comprising at least a plurality of diffusion coordinates, and classifying the data set into a neurological state based on the metrics provided by the diffusion coordinates.

Claims

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

acquiring electrical signals from the brain using at least one electrode channel;

extracting features from the acquired signals; and

classifying the extracted features into one or more diagnostic categories;

wherein the steps of extracting features and classifying extracted features is performed using diffusion geometric analysis.

2. The method of claim 1, further comprising the steps of:

amplifying the acquired brain electrical signals;

digitizing the amplified brain electrical signals to obtain a digital data set.

3. The method of claim 1, wherein the electrical signals from the brain comprises spontaneous electrical activity.

4. The method of claim 1, wherein the electrical signals from the brain comprises evoked potentials.

5. The method of claim 1, wherein the electrical signals from the brain comprises spontaneous electrical activity and evoked potentials.

6. The method of claim 1, wherein the step of extracting features comprises the steps of:

organizing an acquired data set into a collection of digital documents, wherein each document comprises a time window of features of the measurement of each electrode channel;

determining a left and a right singular vector for each digital document;

constructing an affinity matrix using the left or right singular vector to determine affinity between at least two digital documents of the data set;

determining the eigenvectors of the affinity matrix to compute a diffusion geometry of the data set comprising at least a plurality of diffusion coordinates;

embedding of the data into the first three diffusion coordinates to generate a diffusion map, wherein diffusion distances are encoded as Euclidean distances.

7. The method of claim 6, wherein a time-stamped digital document is an M×

L matrix, wherein M is the number of electrode channels and L is the length of the time window.

8. The method of claim 6, wherein the features comprise temporal features.

9. The method of claim 8, wherein the temporal features are wavelet packet features.

10. The method of claim 6, wherein the features comprise spectral features.

11. The method of claim 6, wherein the affinity matrix is a bi Markov matrix.

12. The method of claim 6, further comprising the step of configuring a measurement space through the diffusion distances in order to extract a tabulation of measurement states and their temporal dynamics.

13. The method of claim 6, wherein the diffusion coordinates are stored in a database.

14. The method of claim 13, wherein the stored diffusion coordinates of a plurality of data sets are used as inputs in a diagnostic test.

15. The method of claim 6, wherein classifying the data set comprises partitioning predetermined spaces in the diffusion coordinates space into partitions corresponding to particular neurological states.

16. The method of claim 15, further comprising the step of determining the probability of transition between classified states.

17. The method according to claim 1, further comprising a step of:

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

18. The method of claim 1, further comprising a step of denoising the acquired signals by computing a diffusion geometry of a data set corresponding to the acquired signal, wherein the diffusion geometry comprises at least a plurality of diffusion coordinates.

19. The method of claim 18, wherein an affinity matrix is used to define non-linear filters for denoising and artifact detection.

20. The method of claim 19, further comprising a step of subtracting detected artifacts to obtain a clean brain electrical signal.

21. The method of claim 19, wherein denoising further comprises:

describing a particular feature of the data set as a function;

filtering the function using the affinity matrix to produce a filtered function;

determining diffusion distances between the function and the filtered function; and

extracting particular features wherein the diffusion distance is greater than a pre-determined threshold.

22. A method of classifying a neurological state of subject using diffusion geometry of a data set.

23. The method of claim 22, wherein classifying the neurological state comprises the step of comparing the data set to another data set based on diffusion geometry associated with each dataset.

24. An apparatus for signal processing, comprising:

a signal receiver unit or a headset;

a handheld base unit, wherein

a processor is configured to utilize one or more operating instructions stored in a memory to perform denoising of the received signal, extract features, and classify the signal using diffusion geometric analysis;

a display unit, wherein a result from the processor is displayed.

25. The apparatus of claim 24, wherein the said signal receiver unit comprises at least one electrode, at least one analog amplification channel, and an analog-to-digital converter.

26. The apparatus of claim 24, wherein the signal receiver unit and the handheld base unit are directly connected.

27. The apparatus of claim 24, wherein the signal receiver unit communicates wirelessly with the handheld base unit.

28. The apparatus of claim 27, wherein the signal receiver unit further comprises a wireless transmitter.

29. The apparatus of claim 27, wherein the handheld base unit further comprises a wireless receiver.

30. The apparatus of claim 24, wherein the display unit is operatively connected to the processor to display results of one or more operations performed by the processor;

and

wherein the display unit can be integrated into the handheld base unit, or can be external to the handheld base unit.

31. The apparatus of claim 24, wherein the signal receiver unit and the handheld base unit may be included in a single kit for field use or point-of-care applications.

32. The apparatus of claim 25, wherein the signal receiver unit and the handheld base unit may be configured to reside on a common platform;

and

wherein the at least one analog amplification channel, the analog-to-digital converter, and the processor are integrated into a single chip.

33. The apparatus of claim 24, wherein the processor is configured to compute diffusion geometry of a data set comprising at least a plurality of diffusion coordinates.

34. The apparatus of claim 33, wherein the memory is configured to store the diffusion coordinates.