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

Author

Listed:
  • Manal Tantawi

    (Faculty of Computer and Information Sciences, Ain Shams University, Egypt)

  • Aya Naser

    (Faculty of Computer and Information Sciences, Ain Shams University, Egypt)

  • Howida Abd-Alfatah Shedeed

    (Faculty of Computer and Information Sciences, Ain Shams University, Egypt)

  • Mohammed Fahmy Tolba

    (Faculty of Computer and Information Sciences, Ain Shams University, Egypt)

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.

Suggested Citation

  • Manal Tantawi & Aya Naser & Howida Abd-Alfatah Shedeed & Mohammed Fahmy Tolba, 2021. "Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 12(3), pages 96-115, May.
    • Handle: RePEc:igg:jssmet:v:12:y:2021:i:3:p:96-115
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