Channel-Weighted Squeeze-and-Excitation Networks For Epileptic Seizure Detection

Epilepsy is a chronic neurological disorder that affects many people in the world. Automatic epileptic detection based on multi-channel electroencephalogram (EEG) signals is of great significance and has been widely studied. Recent deep learning models fail to consider the weights of different EEG channels since a few channels can play more important roles than other ones. In this paper, we propose an end-to-end epilepsy detection model, CW-SRNet, to solve this problem. We design a novel channel-weighted block (CW-Block) to capture the different importance of EEG channels automatically and dynamically. We combine our novel CW-Block with the squeeze-and-excitation residual network to improve epilepsy detection performance. Experiments on two public EEG datasets show that our model achieves state-of-the-art performance. Particularly on the CHB-MIT dataset, our model achieves an average sensitivity of 96.84% and an average specificity of 99.68%, outperforming other methods with clear margins.