Epileptic seizure detection and prediction by self-similar methods

• US 20020035338A1
• Filed: 11/30/2000
• Published: 03/21/2002
• Est. Priority Date: 12/01/1999
• Status: Abandoned Application

• Current Assignee: Penn State Research Foundation
• Original Assignee: Penn State Research Foundation
• Inventors: Hart, Corey B., Dear, Stephen P.
• Application Number:
  US09/727,383
• Publication Number:
  US 20020035338A1
• 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 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/726 using Wavelet transforms

Abstract

The present invention provides a system and method for the detection and prediction of epileptic seizures based on the calculation of a scaling exponent, scaling behavior, and fractal fraction values from a patient'"'"'s brain wave activity data.

Claims

1. A method for epileptic seizure warning comprising:

a) receiving electrical patterns data of a patient'"'"'s brain waves;

b) computing a scaling exponent from the electrical patterns data; and

c) rendering a notification when said scaling exponent satisfies preselected parameters.

2. The method of claim 1 wherein said electrical patterns data includes electroencephalogram recording data.

3. The method of claim 2 wherein said preselected parameters include a scaling exponent value >

1 sustained for a predetermined time interval.

4. The method of claim 3 wherein said time interval is greater than 1 second.

5. The method of claim 3 wherein said time interval is between 1 second and 10 minutes.

6. The method of claim 3 wherein said time interval is greater than 3 seconds.

7. The method of claim 3 wherein said time interval is greater than 10 seconds.

8. The method of claim 3 wherein said time interval is greater than 30 seconds.

9. The method of claim 3 wherein said time interval is greater than 1 minute.

10. The method of claim 3 wherein said time interval is greater than 2 minutes.

11. The method of claim 1 wherein said preselected parameters include a scaling exponent value >

1 sustained for a predetermined time interval.

12. The method of claim 1 wherein said preselected parameters include a scaling exponent value >

1 sustained for a first predetermined time interval and a subsequent decrease of the scaling exponent value to <

1 during a second predetermined time interval.

13. The method of claim 12 wherein said first predetermined time interval is between 1 second and 10 minutes and said second predetermined time interval is less than 2 minutes.

14. The method of claim 13 wherein said second predetermined time interval is less than 1 minute.

15. The method of claim 14 wherein said second predetermined time interval is less than 30 seconds.

16. The method of claim 15 wherein said second predetermined time interval is less than 10 seconds.

17. The method of claim 16 wherein said second predetermined time interval is less than 5 seconds.

18. The method of claim 1 wherein said preselected parameters include a rapid rise in scaling exponent values to >

2 over a predetermined time interval and a subsequent decrease of scaling behavior values to about 0 during said time interval.

20. The system of claim 18 wherein said means for receiving electrical patterns data includes an electroencephalogram recording.

21. The system of claim 19 wherein said calculating means is a scaling exponent calculation program running on a microprocessor.

22. The system of claim 18 wherein said predetermined parameters include a scaling exponent value of >

1 sustained for a predetermined time interval.

23. The system of claim 18 wherein said preselected parameters include a scaling exponent value >

1 sustained for a first predetermined time interval and a subsequent decrease of the scaling exponent value to <

1 during a second predetermined time interval.

24. The system of claim 20 wherein said predetermined parameters include a scaling exponent value of >

1 sustained for a predetermined time interval

25. The system of claim 20 wherein said preselected parameters include a scaling exponent value >

1 sustained for a first predetermined time interval and a subsequent decrease of the scaling exponent value to <

1 during a second predetermined time interval.

26. The system of claim 24 wherein said first predetermined time interval is between 1 second and 10 minutes and said second predetermined time interval is less than 2 minutes.

27. The method of claim 23 wherein said time interval is between 1 second and 10 minutes.

19. A system for epileptic seizure warning comprising:

a) means for receiving electrical patterns data of a patient'"'"'s brain waves;

b) calculating means for computing a scaling exponent from the electrical patterns data; and

c) warning means to provide a notification when said scaling exponent satisfies predetermined parameters.

28. A method for treating a patient experiencing epileptic seizures comprising:

a) receiving electrical patterns data of a patient'"'"'s brain waves;

b) computing scaling exponent values from the electrical patterns data for a predetermined time interval; and

c) determining a treatment regimen for the patient based on said scaling exponent values.

29. A method for treating a patient experiencing epileptic seizures comprising:

a) receiving electrical patterns data of a patient'"'"'s brain waves;

b) computing scaling behavior values from the electrical patterns data for a predetermined time interval; and

c) determining a treatment regimen for the patient based on said scaling behavior values.

30. A method for treating a patient experiencing epileptic seizures comprising:

a) receiving electrical patterns data of a patient'"'"'s brain waves;

b) computing fractal fraction values from the electrical patterns data for a predetermined time interval; and

c) determining a treatment regimen for the patient based on said fractal fraction values.

31. The method of claim 30 further comprising the step of mapping seizure evolution based on the fractal fraction values, prior to step (c).

33. The method of claim 31 wherein said electrical patterns data includes electroencephalogram recording data.

32. A method for epileptic seizure warning comprising:

a) receiving electrical patterns data of a patient'"'"'s brain waves;

b) computing a fractal fraction value from the electrical patterns data; and

c) rendering a notification when said fractal fraction value satisfies pre-selected parameters.

34. The method of claim 32 wherein said pre-selected parameters include a fractal fraction value ≦

10<sup>−

5</sup> for a predetermined length of time.