METHOD AND APPARATUS FOR REDUCING THE NUMBER OF CHANNELS IN AN EEG-BASED EPILEPTIC SEIZURE DETECTOR

• US 20090082689A1
• Filed: 08/22/2008
• Published: 03/26/2009
• Est. Priority Date: 08/23/2007
• Status: Abandoned Application

• Current Assignee: Massachusetts Institute of Technology
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Glassman, Elena L., Shih, Eugene I., Shoeb, Ali, Guttag, John V.
• Application Number:
  US12/196,690
• Publication Number:
  US 20090082689A1
• 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 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy

Abstract

An ambulatory patient-specific epileptic seizure detector based on scalp EEG signals is presented. A method for selecting a patient-specific subset of electrodes from a plurality of m EEG channels needed to detect an epileptic seizure in the patient is also presented. Seizure EEG data is collected from the plurality of m EEG channels. An effective subset n of the channels of the plurality of m EEG channels is selected using recursive feature processing and a detector is constructed in response to the subset n of channels. The performance of the detector in detecting seizures is then estimated.

Claims

1. A method for selecting a patient-specific subset of electrodes from a plurality of m EEG channels needed to detect an epileptic seizure in the patient, the method comprising the steps of:

collecting seizure EEG data from the plurality of m EEG channels;

selecting an effective subset n of the channels of the plurality of m EEG channels;

constructing a detector in response to the subset n of channels; and

estimating the performance of the detector in detecting seizures.

2. The method of claim 1 wherein the step of selecting the effective subset n of the channels of the plurality of m EEG channels comprises recursive feature elimination.

3. The method of claim 2 wherein recursive feature elimination comprises the steps of:

a. constructing a detector using the plurality of m channels;

b. estimating the performance of the detector;

c. removing a least useful channel from the plurality of m channels;

d. estimating the performance of the remaining plurality of channels;

e. repeating steps c and d until the performance of the remaining plurality of channels satisfies a criterion; and

f. setting n equal to the number of channels in the plurality of channels equal to one more than the number of channels in the plurality of channels that satisfied the criterion.

4. The method of claim 3 wherein the criterion comprises the performance of the remaining plurality of channels being worse than the performance of the plurality of m channels.

5. The method of claim 1 wherein the step of selecting the effective subset n of the channels of the plurality of m EEG channels comprises recursive feature addition.

6. The method of claim 5 wherein recursive feature addition comprises the steps of:

a. constructing a detector using one of the plurality of m channels;

b. estimating the performance of the detector;

c. adding a most useful channel from the plurality of m channels to the subset n;

d. estimating the performance of the plurality of channels in subset n;

e. repeating steps c and d until the performance of the plurality of channels in subset n satisfies a criterion; and

f. setting n equal to the number of channels in the plurality of channels that satisfied the criterion.

7. The method of claim 6 wherein the criterion comprises the performance of the remaining plurality of channels being no worse than the performance of the plurality of m channels.

8. The method of claim 1 wherein estimating the performance of the detector comprises evaluating at least one of the group consisting of false positive rate, false negative rate and latency.

9. The method of claim 1 wherein the step of estimating the performance of the detector is done with a cross-validation methodology.

10. The method of claim 1 wherein the detector is a support vector machine based detector.

11. The method of claim 10 wherein the support vector machine based detector comprises a radial basis kernel.

12. The method of claim 11 wherein the radial basis kernel is non-linear.

13. A patient-specific epileptic seizure detector comprising:

a plurality of electrodes corresponding to a plurality of m EEG channels;

a processor configured to select a subset n of the channels of the plurality of m EEG channels using recursive feature elimination, the detector constructed in response to the subset n of channels; and

an estimator configured to estimate the performance of the detector in detecting seizures.

14. The detector of claim 13 wherein the subset n comprises the plurality of m channels minus a plurality of least useful channels, whereby the least useful channels are determined by recursively removing the least useful channel from the plurality of m channels and estimating the performance of the remaining plurality of channels until the performance of the remaining plurality of channels satisfies a criterion, the subset n equal to the number of channels in the plurality of channels equal to one more than the number of channels in the plurality of channels that satisfied the criterion.

15. The detector of claim 13 wherein the estimator estimates the performance of the detector from at least one of the group consisting of a false positive rate, a false negative rate and latency.

16. The detector of claim 13 wherein the detector is a support vector machine based detector.

17. The detector of claim 16 wherein the support vector machine based detector comprises a radial basis kernel.

18. The detector of claim 17 wherein the radial basis kernel is non-linear.

19. A patient-specific epileptic seizure detector comprising:

a plurality of electrodes corresponding to a plurality of m EEG channels;

a processor configured to select a subset n of the channels of the plurality of m EEG channels using recursive feature addition, the detector constructed in response to the subset n of channels; and

an estimator configured to estimate the performance of the detector in detecting seizures.

20. The detector of claim 19 wherein the subset n comprises the plurality of m channels minus a plurality of least useful channels, whereby the subset n is determined incrementally by adding a most useful channel from the plurality of m channels and estimating the performance of the channels in the subset n until the performance of the channels in the subset n satisfies a criterion, the subset n to the number of channels in the plurality of channels that satisfied the criterion.