Epileptic seizure detection from electroencephalogram signals based on 1D CNN-LSTM deep learning model using discrete wavelet transform

Abstract Excessive electrical activity in the brain causes epileptic seizures which can be detected through Electroencephalogram (EEG) signals. The research aims to identify epileptic seizures using EEG records automatically. Firstly, EEG bands are extracted using Discrete Wavelet Transform (DWT) and concatenated. Secondly, the resulting feature vector is fed into a 1-dimensional Convolutional Neural Network (CNN) to extract spatial information. The Long-Short Term Memory (LSTM) layer then receives the feature maps in order to extract the temporal information. Ultimately, a fully connected layer will use the generated spatiotemporal features as input to categorize the signal. Results show that the suggested model performs well on the following datasets: the TUSZ corpus, which has 94. 32% accuracy, 86. 08% Kappa value, and 79. 01% GDR; the BONN dataset, which has 97. 24% accuracy, 97. 92% Kappa value, and 99. 18% GDR; and the CHB-MIT dataset, which has 96. 94% accuracy, 94. 33% Kappa value, and 96. 36% GDR. The computational complexity for BONN, CHB-MIT, and TUSZ datasets are \: 3. 07\: 10^7, \: 1. 67\: 10^6 and \: 1. 67\: 10^6 respectively. The performance of several popular machine learning classifiers is compared with the proposed model. The results show that the model outperforms existing approaches. The model’s strong performance is largely due to the CNN’s ability to effectively extract meaningful spatial features.