A fuzzy logic system for seizure onset detection in intracranial EEG

Authors:
Ahmed Fazle Rabbi;Reza Fazel-Rezai
Affiliations:
Department of Electrical Engineering, University of North Dakota, Grand Forks, ND;Department of Electrical Engineering, University of North Dakota, Grand Forks, ND
Venue:
Computational Intelligence and Neuroscience
Year:
2012

Citing 6
Cited 0

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An automatic patient-specific seizure onset detection method in intracranial EEG based on incremental nonlinear dimensionalityreduction

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Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks

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Abstract

We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted fromintracranial 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 fromremote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived fromthe final fuzzy subsystem. Themethod 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.