A fuzzy rule-based system for epileptic seizure detection in intracranial EEG.
OBJECTIVE
We present a method for automatic detection of seizures in intracranial EEG recordings from patients suffering from medically intractable focal epilepsy.


METHODS
We designed a fuzzy rule-based seizure detection system based on knowledge obtained from experts' reasoning. Temporal, spectral, and complexity features were extracted from IEEG segments, and spatio-temporally integrated using the fuzzy rule-based system for seizure detection. A total of 302.7h of intracranial EEG recordings from 21 patients having 78 seizures was used for evaluation of the system.


RESULTS
The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11s. There was only one missed seizure. Most of false detections were caused by high-amplitude rhythmic activities. The results from the system correlate well with those from expert visual analysis.


CONCLUSIONS
The fuzzy rule-based seizure detection system enabled us to deal with imprecise boundaries between interictal and ictal IEEG patterns.


CONCLUSIONS
This system may serve as a good seizure detection tool with high sensitivity and low false detection rate for monitoring long-term IEEG.
Aarabi
A
A
Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada. aarabi@EE.UManitoba.CA
Fazel-Rezai
R
R
Aghakhani
Y
Y
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D003627:Data Interpretation, Statistical; D004567:Electrodes, Implanted; D004569:Electroencephalography; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis*; D005189:False Positive Reactions; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008875:Middle Aged; D012640:Seizures / Q000175:diagnosis*; D055815:Young Adult