Optimized One-Dimension Convolutional Neural Network for Seizure Classification from EEG Signal based on Whale Optimization Algorithm

Abstract

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. The proposed method is suitable for the children's hospital bostonmassachusetts institute of technology (CHB-MIT) dataset.