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Computational Intelligence and NeuroscienceVolume 2012 2012, Article ID 705140, 12 pages

Research ArticleDepartment of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202, USA

Received 2 July 2011; Revised 1 October 2011; Accepted 4 November 2011

Academic Editor: Francois Benoit Vialatte

Copyright © 2012 Ahmed Fazle Rabbi and Reza Fazel-Rezai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.

Autor: Ahmed Fazle Rabbi and Reza Fazel-Rezai

Fuente: https://www.hindawi.com/


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