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Published on: June 23, 2023
A fuzzy rule-based system for epileptic seizure detection in intracranial EEG
A Aarabi1, R Fazel-Rezai, Y Aghakhani
1Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada. aarabi@EE.UManitoba.CA
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.
Conclusion:
The fuzzy rule-based seizure detection system enabled us to deal with imprecise boundaries between interictal and ictal IEEG patterns.
Significance:
This system may serve as a good seizure detection tool with high sensitivity and low false detection rate for monitoring long-term IEEG.
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