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Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy

Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy

Manal Tantawi, Aya Naser, Howida Abd-Alfatah Shedeed, Mohammed Fahmy Tolba
Copyright: © 2021 |Volume: 12 |Issue: 3 |Pages: 20
ISSN: 1947-959X|EISSN: 1947-9603|EISBN13: 9781799860921|DOI: 10.4018/IJSSMET.2021050106
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MLA

Tantawi, Manal, et al. "Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy." IJSSMET vol.12, no.3 2021: pp.96-115. http://doi.org/10.4018/IJSSMET.2021050106

APA

Tantawi, M., Naser, A., Shedeed, H. A., & Tolba, M. F. (2021). Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 12(3), 96-115. http://doi.org/10.4018/IJSSMET.2021050106

Chicago

Tantawi, Manal, et al. "Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) 12, no.3: 96-115. http://doi.org/10.4018/IJSSMET.2021050106

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Abstract

Electroencephalogram (EEG) signals are a valuable source of information for detecting epileptic seizures. However, monitoring EEG for long periods of time is very exhausting and time consuming. Thus, detecting epilepsy in EEG signals automatically is highly appreciated. In this study, three classes, namely normal, interictal (out of seizure time), and ictal (during seizure), are considered. Moreover, a comparative study is provided for the efficient features in literature resulting in a suggested combination of only three discriminative features, namely R'enyi entropy, line length, and energy. These features are calculated from each of the EEG sub-bands. Finally, support vector machines (SVM) classifier optimized using BAT algorithm (BAT-SVM) is introduced by this study for discriminating between the three classes. Experiments were conducted using Andrzejak database. The accomplished experiments and comparisons in this study emphasize the superiority of the proposed BAT-SVM along with the suggested feature set in achieving the best results.

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