Brain Activity as a Marker of Disease

• US 20110112426A1
• Filed: 11/10/2009
• Published: 05/12/2011
• Est. Priority Date: 11/10/2009
• Status: Abandoned Application

• Current Assignee: Brainscope SPV LLC
• Original Assignee: Brainscope Company Inc.
• Inventors: Causevic, Elvir
• Application Number:
  US12/615,423
• Publication Number:
  US 20110112426A1
• Time in Patent Office: Days
• 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 5/4076 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: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A method of monitoring brain activity is provided, wherein the method includes receiving a signal associated with neuronal activity of a mammalian brain. The method also includes processing the signal using a linear, non-linear, or combination algorithm to extract a signal feature. A neuromarker may be determined based on an association between the signal feature and a library of features, wherein the library includes a plurality of signal features correlated with a plurality of disease states.

Claims

1. A method of monitoring brain activity, comprising:

receiving a signal of brain electrical activity from a patient;

processing the signal using at least one processor implementing an algorithm to extract a signal feature based on a brain state; and

determining a neuromarker using at least one processor, wherein the neuromarker is determined based on an association between the signal feature and a disease state.

2. The method of claim 1, wherein the neuromarker corresponds to a particular state of progression of disease

3. The method of claim 1, wherein the neuromarker corresponds to a particular disease stage.

4. The method of claim 1, wherein the neuromarker represents a discrete change of state in disease progression.

5. The method of claim 1, wherein the neuromarker represents an indicator of an appropriate time to apply treatment.

6. The method of claim 1, wherein the neuromarker represents an indicator of effectiveness of applied treatment.

7. The method of claim 1, wherein the algorithm is based on at least one of wavelet analysis, diffusion geometric analysis, fractal analysis, and spectral analysis.

8. The method of claim 1, wherein the neuromarker is correlated with a traditional disease state marker.

9. The method of claim 8, wherein the correlation uses a mutual information algorithm.

10. A method of monitoring brain activity, comprising:

receiving a signal of brain electrical activity from a patient;

processing the signal using at least one processor implementing a non-linear algorithm to extract a signal feature; and

determining a neuromarker using at least one processor based on an association between the signal feature and a library of features stored in a memory, wherein the library includes a plurality of signal features correlated with a plurality of disease states.

11. The method of claim 10, wherein the neuromarker includes a discrete neuromarker.

12. The method of claim 10, wherein the signal includes an electro-encephalography signal.

13. The method of claim 10, wherein the non-linear algorithm is based on at least one of wavelet analysis, diffusion geometric analysis, fractal analysis, and spectral analysis.

14. The method of claim 10, further including pre-processing the signal, wherein pre-processing includes at least one of denoising, filtering, windowing, sampling, and digitizing.

15. The method of claim 10, wherein the library of features is at least partially determined using a genetic algorithm.

16. The method of claim 10, further including applying a treatment to the patient.

17. A system configured to display a neuromarker, comprising:

a receiver configured to receive a signal associated with neuronal activity of a patient'"'"'s brain;

a processor configured to process the signal using a non-linear algorithm to extract a signal feature and determine a neuromarker based on an association between the signal feature and a library of features;

a storage system configured to store the library of features, wherein the library includes a plurality of signal features correlated with a plurality of disease states; and

a display system configured to display a representation of the neuromarker.

18. The system of claim 17, wherein the signal includes an electro-encephalography signal.

19. The system of claim 18, where the signal includes a high frequency band.

20. The system of claim 17, wherein the non-linear algorithm is based on at least one of wavelet analysis, diffusion geometric analysis, fractal analysis, and spectral analysis.

21. The system of claim 17, wherein the processor is further configured to pre-process the signal, wherein pre-processing includes at least one of denoising, filtering, windowing, sampling, and digitizing.

22. The system of claim 17, wherein the library of features is at least partially determined using a genetic algorithm.

23. The system of claim 17, wherein the system is further configured to apply a treatment to the patient.

24. A method of creating a library of features, comprising:

receiving a signal associated with neuronal activity of a mammalian brain;

processing the signal using at least one processor implementing a linear, a non-linear, or a combination algorithm to extract a signal feature;

associating the signal feature with a disease state; and

storing in a memory the signal feature and the disease state in the library of features.

25. The method of claim 24, wherein the signal includes an electro-encephalography signal.

26. The method of claim 25, where the signal includes a high frequency band.

27. The method of claim 24, wherein the non-linear algorithm is based on at least one of wavelet analysis, diffusion geometric analysis, fractal analysis, and spectral analysis.

28. The method of claim 24, further including pre-processing the signal, wherein pre-processing includes at least one of denoising, filtering, windowing, sampling, and digitizing.

29. The method of claim 24, wherein associating the signal feature with the disease state includes at least one of a statistical association, a correlation, and a comparison.

30. The method of claim 24, wherein the library of features is at least partially created using a genetic algorithm.

31. A method of assessing a neuromarker, comprising:

providing a human brain with a known disease state;

receiving a signal associated with neuronal activity of the human brain;

processing the signal using at least one processor implementing a non-linear algorithm to extract a signal feature;

correlating the signal feature with the known disease state using at least one processor; and

identifying a neuromarker based on the signal feature using at least one processor.

32. The method of claim 31, wherein the signal includes an electro-encephalography signal.

33. The method of claim 31, further including pre-processing the signal, wherein pre-processing includes at least one of denoising, filtering, windowing, sampling, and digitizing.

34. The method of claim 31, wherein the non-linear algorithm is based on at least one of wavelet analysis, diffusion geometric analysis, fractal analysis, and spectral analysis.

35. The method of claim 31, further including validating the neuromarker, comprising:

providing a biological indicator associated with the known disease state; and

correlating the neuromarker with the biological indicator.

36. The method of claim 35, wherein the biological indicator includes at least one of imaging data, chemical data, molecular data, and patient test data.

37. The method of claim 36, wherein validating the neuromarker further includes correlating the known disease state with at least one of age, gender, physical condition, and mental state.