Epilepsy is a chronic disorder that causes sudden, recurring seizures and early detection of seizures is needed for prompt treatment to reduce the higher risk. An electroencephalogram (EEG) can detect epilepsy based on traces of electrical activity and wave patterns in the brain. However, analyzing EEG signals takes a long time and is operated by neuroscientists. In this paper, we propose automatic seizure detection using a one-dimension convolutional neural network (1D CNN) and the approach of whale optimization algorithm (WOA). The EEG signal is trimmed every three seconds, and features are extracted using discrete wavelet transform (DWT). The WOA approach was used to optimize the number of layers and neurons in 1D CNN. The experimental results show that the proposed model can improve CNN’s performance in detecting seizures with an accuracy of 99.76%, respectively.

Original languageEnglish
Pages (from-to)310-322
Number of pages13
JournalInternational Journal of Intelligent Engineering and Systems
Issue number3
Publication statusPublished - 2023


  • Convolutional neural network (CNN)
  • Discrete wavelet transform (DWT)
  • Electroencephalography (EEG)
  • Epilepsy
  • Whale optimization algorithm (WOA)


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