We present a fuzzy rule-based system for epileptic seizure onset detection. Two features based on temporal evolution of seizure in electroencephalogram (EEG) were extracted from intracranial EEG (iEEG) recordings. Features extracted from multichannel EEGs were combined using fuzzy algorithms in feature domain as well as in spatial (channels) domain. Fuzzy rules were derived from experts’ knowledge and reasoning. Finally, a predefined threshold was used to make the final decision. A total of 40.46 h of iEEG recordings (obtained from Freiburg Seizure Prediction EEG database) selected from 13 patients having 19 seizures was used for the system evaluation. The overall detection rate of 100% was achieved with false detection rate of 0.275/h and the average detection latency of 26.858 seconds.
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An Approach to Seizure Onset Detection Using Fuzzy Logic Based on Seizure Evolution in Intracranial EEG
Reza Fazel-Rezai
Reza Fazel-Rezai
University of North Dakota
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Ahmed Rabbi
University of North Dakota
Reza Fazel-Rezai
University of North Dakota
J. Med. Devices. Jun 2011, 5(2): 027537 (1 pages)
Published Online: June 15, 2011
Article history
Online:
June 15, 2011
Published:
June 15, 2011
Citation
Rabbi, A., and Fazel-Rezai, R. (June 15, 2011). "An Approach to Seizure Onset Detection Using Fuzzy Logic Based on Seizure Evolution in Intracranial EEG." ASME. J. Med. Devices. June 2011; 5(2): 027537. https://doi.org/10.1115/1.3591388
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