System for the prediction, rapid detection, warning, prevention, or control of changes in activity states in the brain of a subject

• US 20100198098A1
• Filed: 12/05/2009
• Published: 08/05/2010
• Est. Priority Date: 01/06/1997
• Status: Abandoned Application

• Current Assignee: David Large, Ivan Osorio, John Ralston, Josef Dorfmeister, Mark Frei
• Original Assignee: David Large, Ivan Osorio, John Ralston, Josef Dorfmeister, Mark Frei
• Inventors: Lerner, David, Dorfmeister, Josef, Ralston, John, Osorio, Ivan, Frei, Mark
• Application Number:
  US12/592,990
• Publication Number:
  US 20100198098A1
• 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 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61N 1/36064 Epilepsy
• CPC Class Codes: A61N 1/36082 Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease

Abstract

A system (10) analyzes signals representative of a subject'"'"'s brain activity in a signal processor (12) for information indicating the subject'"'"'s current activity state and for predicting a change in the activity state. One preferred embodiment uses a combination of nonlinear filtering methods to perform real-time analysis of the electro-encephalogram (EEG) or electro-corticogram (ECoG) signals from a subject patient for information indicative of or predictive of a seizure, and to complete the needed analysis at least before clinical seizure onset. The preferred system then performs an output task for prevention or abatement of the seizure, or for recording pertinent data.

Claims

1. A method of detecting the occurrence of abnormal activity in the brain of a subject, said method comprising the steps of:

(a) receiving into a signal processor input signals indicative of the subject'"'"'s brain activity;

(b) determining ictal components in said input signals by applying to said input signals a first filter configured for extracting and enhancing ictal components from said input signals;

(c) measuring the ictal activity in a foreground epoch of said input signals by applying an order-statistic filter to said ictal components corresponding to said epoch to produce a foreground measure of ictal activity;

(d) determining whether said foreground measure reaches a threshold level, such being indicative of the occurrence of said abnormal activity and

(e) performing steps (a)-(d) while said abnormal activity is occurring.

2. The method as set forth in claim 1, step (b) including the step of using a digital filter as said first filter.

3. The method as set forth in claim 2, step (b) including the step of using a finite impulse response filter as said digital filter.

4. The method as set forth in claim 2, step (b) including the step of using an infinite impulse response filter as said digital filter.

5. The method as set forth in claim 1, step (b) including the step of using an analog filter as said first filter.

6. The method as set forth in claim 1, step (b) including the step of squaring the results of the application of said first filter to produce said ictal components.

7. The method as set forth in claim 1,

step (c) including the step of measuring the ictal activity in a background epoch of said input signals by applying said order-statistic to said ictal components corresponding to said background epoch to produce a background measure of ictal activity, said background epoch occurring before said foreground epoch,

step (d) including the step of determining whether the ratio of said foreground measure to said background measure reaches said threshold level.

8. The method as set forth in claim 7, step (c) including the step of configuring said foreground and background epochs to present a time delay therebetween.

9. The method as set forth in claim 8, step (c) including the step configuring said foreground epoch as about two seconds, said background epoch as about twenty seconds and said delay as about one second.

10. The method as set forth in claim 7, step (c) including the step of continuously updating said foreground and background measures.

11. The method as set forth in claim 10, said threshold level being a first threshold level, step (c) including the step of suspending updating said background measure if said ratio reaches a second threshold level.

12. The method as set forth in claim 1, step (e) including the step of performing steps (a)-(d) before the onset of the clinical component of the seizure thereby being predictive of the clinical component.

13. The method as set forth in claim 12, step (e) including the step of performing steps (a)-(d) before the onset of the electrographic component of the seizure thereby being predictive of the electrographic component.

14. The method as set forth in claim 1, step (b) including the step of selecting said first filter from a filter bank including a plurality of filters configured for extracting and enhancing said ictal components.

15. The method as set forth in claim 14, step (b) including the step of selecting said first filter as the filter from said filter bank providing the greatest differentiation of ictal components.

16. The method as set forth in claim 15, said signal processor including memory means for storing said filter bank, step (b) including the step of retrieving a plurality of filters from said filter bank and applying said plurality of filters to said input signals in said signal processor and therein selecting said first filter as the filter from said plurality providing the greatest differentiation of ictal components.

17. The method as set forth in claim 1, further including the step of configuring said first filter in order to increase the differentiation of ictal components for the subject.

18. The method as set forth in claim 1,

step (b) including the steps of using one of a finite impulse response filter and an infinite impulse response filter as said digital filter, and squaring the results of the application of said first filter to enhance said ictal components,

step (c) including the steps of measuring the ictal activity in a background epoch of said input signals by applying said order-statistic to said ictal components corresponding to said background epoch to produce a background measure of ictal activity and continuously updating said foreground and background measures, said background epoch occurring before said foreground epoch and said foreground and background epochs presenting a time delay therebetween,

step (d) including the step of determining whether the ratio of said foreground measure to said background measure reaches said threshold level,

said threshold level being a first threshold level, step (c) including the step of suspending updating said background measure if said ratio reaches a second threshold level.

19. The method as set forth in claim 1, step (a) including the step of receiving said signals from at least one electrode operable for detecting the subject'"'"'s brain activity and for producing said signals indicative thereof, said at least one electrode being selected from the group consisting of a scalp electrode and an implanted electrode.

20. The method as set forth in claim 1, step (a) including the step of receiving said signals from a memory device.

21. The method as set forth in claim 1, step (b) including the step of analyzing said signals in said signal processor selected from the group consisting of a microprocessor and a computer.

22. The method as set forth in claim 1, step (b) including the step of analyzing said signals in a signal processor implanted within the subject.

23. The method as set forth in claim 1, step (b) including the steps of analyzing said signals by using a wavelet filter as said first filter to determine corresponding wavelet coefficients of said signals, using said wavelet coefficients to determine a power density distribution, and comparing said power density distribution with said threshold level wherein the crossing of said threshold level by said distribution indicates the occurrence of the seizure.

24. The method as set forth in claim 1, step (b) including the step of analyzing said signals using windowed Fourier and inverse Fourier transforms as said first filter.

25. The method as set forth in claim 1 including the step of producing an output in response to the indication of the occurrence of a seizure as said abnormal activity with said output taken from the group consisting of administering a medicament, electrically stimulating a portion of the subject'"'"'s brain, magnetically stimulating a portion of the subject'"'"'s brain, inhibiting activity in a portion of the subject'"'"'s brain, electrically stimulating a nerve of the subject, recording said signals, activating an alert, stimulating physiological receptors of the patient, heating at least a portion of the subject'"'"'s brain, cooling at least a portion of the subject'"'"'s brain, facilitating activity in a portion of a subject'"'"'s brain, disfacilitating activity in a portion of a subject'"'"'s brain, and ablating a portion of the subject'"'"'s brain.

26. The method as set forth in claim 25, further including the step of using a device implanted with the subject for producing said output.

27. The method as set forth in claim 1, said first filter including a genetically designed filter.

34. The method as set forth in claim 1, step (b) including the step of using a filter as said first filter taken from the group consisting of a digital filter, a nonlinear filter, and adaptive filter, a correlation integral, an arc length differential and a temperature filter.

35. The method as set forth in claim 1, said method including the method of detecting the occurrence of an epileptic seizure as said seizure.

36. The method as set forth in claim 1 further including the step of receiving other biological signals concerning the subject and using said biological signals in detecting the occurrence of a seizure, said biological signals being representative of biological functions of the subject selected from the group consisting of respiratory activity, concentrations of glucose, free radicals, neurotransmitters, blood, body tissues, brain temperature, heart activity, muscle activity, ocular activity, magnetic fields, skin resistance, and electrical fields.

37. The method as set forth in claim 1 further including the step of providing biofeedback to the subject indicative of a seizure or epileptiform discharge.

38. The method as set forth in claim 1, including the step of measuring said ictal activity in said foreground epoch against a background signal generated using time and state-weighted averaging.

39. The method as set forth in claim 38, including the step of using an exponentially forgetting time averaging as said time and state-weighted averaging.

28. A method of predicting the occurrence of a seizure in the brain of a subject, said method comprising the steps of:

(a) receiving into a signal processor input signals indicative of the subject'"'"'s brain activity;

(b) analyzing said signals for at least one precursor predictive of the occurrence of a seizure in the subject; and

(c) upon occurrence of said at least one precursor, producing an output in response before the occurrence of the seizure.

29. The method as set forth in claim 28, step (b) including the step of detecting epileptiform discharges in said signals, determining the sharpness of each of said discharges by determining a parabola of optimal fit for each of said spikes, determining an index of relative sharpness of each of said discharges by comparing each sharpness to the sharpness of other discharges in a time window, determining whether said index reaches said predetermined level, said spike presenting a polarity, and determining whether said spikes reaching said predetermined level fit a predetermined pattern, such being predictive of a seizure.

30. The method as set forth in claim 29, step (b) including the step of determining said precursor by detecting epileptiform spikes by using a signal analysis filter to extract spike shape coefficients, determining a ratio of spike shape coefficients squared to background signal information, determining whether said ratio exceeds said predetermined level and if so, determining whether spikes exceeding said ratio fit a pattern determined as being predictive of a seizure.

31. The method as set forth in claim 28, step (b) including the step of determining said precursor by determining a ratio of current signal energy compared to background energy and determining whether said ratio exceeds said predetermined level, such being predictive of a seizure.

32. The method as set forth in claim 28, step (b) including the step of determining said precursor by determining a ratio of median frequency to background median frequency and determining whether said ratio exceeds said predetermined level, such being predictive of a seizure.

33. A method of analyzing the activity state of the brain of a subject, said method comprising the steps of:

(a) receiving into a signal processor signals representative of the subject'"'"'s brain activity;

(b) analyzing said signals in said signal processor for presence of information indicating the current activity state of the subject'"'"'s brain;

(c) accomplishing step (b) while said activity state is occurring; and

(d) producing an output in response to said presence in said information.

40. A seizure detection, prediction and treatment apparatus for detecting the occurrence of a seizure in the brain of a subject, said method comprising:

receiving means for receiving input signals indicative of the subject'"'"'s brain activity;

signal processing means coupled with said receiving means for

determining ictal components in said input signals by applying to said input signals a first filter configured for extracting and enhancing ictal components from said input signals,

measuring the ictal activity in a foreground epoch of said input signals by applying an order-statistic filter to said ictal components corresponding to said epoch to produce a foreground measure of ictal activity, and

determining whether said foreground measure exceeds a predetermined level, such being indicative of the occurrence of a seizure, and

output means for producing an output indicating the occurrence of the seizure while the seizure is occurring.