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Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals

Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals

Abduljalil Mohamed, Khaled Bashir Shaban, Amr Mohamed
Copyright: © 2012 |Volume: 3 |Issue: 2 |Pages: 18
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466613225|DOI: 10.4018/jkdb.2012040103
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MLA

Mohamed, Abduljalil, et al. "Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals." IJKDB vol.3, no.2 2012: pp.27-44. http://doi.org/10.4018/jkdb.2012040103

APA

Mohamed, A., Shaban, K. B., & Mohamed, A. (2012). Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 3(2), 27-44. http://doi.org/10.4018/jkdb.2012040103

Chicago

Mohamed, Abduljalil, Khaled Bashir Shaban, and Amr Mohamed. "Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 3, no.2: 27-44. http://doi.org/10.4018/jkdb.2012040103

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Abstract

Different brain states and conditions can be captured by electroencephalogram (EEG) signals. EEG-based epileptic seizure detection techniques often reduce these signals into sets of discriminant features. In this work, an evidence theory-based approach for epileptic detection, using several classifiers, is proposed. Within the framework of the evidence theory, each of these classifiers is considered a source of information and given a certain weight based on both its overall classification accuracy as well as its precision rate for the respective brain state. These sources are fused using the Dempster’s rule of combination. Experimental work is done where five time domain features are obtained from EEG signals and used by a set classifiers, namely, Bayesian, K-nearest neighbor, neural network, linear discriminant analysis, and support vector machine classifiers. Higher classification accuracy of 89.5% is achieved, compared to 75.07% and 87.71% accuracy obtained from the worst and best used classifiers.

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