title: A fuzzy logic system for seizure onset detection in intracranial EEG.
abstract: We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.
authors.affiliation: Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202, USA.
authors.forename: Ahmed Fazle
authors.initials: AF
authors.lastname: Rabbi
authors.forename: Reza
authors.initials: R
authors.lastname: Fazel-Rezai
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D003198:Computer Simulation; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods* / Q000592:standards; D004827:Epilepsy / Q000175:diagnosis* / Q000188:drug therapy / Q000503:physiopathology*; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008959:Models, Neurological; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity