The EEG-based Automatic Epilepsy Detection Using Multi-scale Multi-level Temporal-Spatial Attention CNN model

The automatic detection of epileptic EEG signals based on deep learning has garnered significant attention due to its ability to mitigate the influence of human factors. The effectiveness of epilepsy detection primarily hinges on the architecture of deep neural network models. Thus, this study proposes a Multi-Scale Multi-Level Temporal-Spatial Attention CNN model (MSMLTSACNN), which includes the Multi-Scale Multi-Level Attention Module (MSMLAM), the Spatial-Temporal Feature Extraction Module (STFEM), the Scale Feature Reconstruction Module (SFRM) and the Classification Module (CM). Firstly, MSMLAM is used to extract the multi-scale features of epileptic EEG signals. Then, for each scale feature, the information flow between electrodes is acquired at three hierarchical levels, and the three-level features of the current scale are integrated by using the attention-weighted mechanism. Furthermore, STFEM, SFRM and CM are employed successively to extract, rerepresent and classify the multi-scale temporal-spatial features. Through five-fold cross-validation experiments on the CHB-MIT dataset, the MSMLTSACNN model achieved favorable results in terms of sensitivity (98.97%), F1 score (98.68%), accuracy (98.78%), and precision (98.57%). The results indicate that MSMLTSACNN can effectively improves epileptic seizure detection performance by extracting more representative multi-scale features based on the multi-level information flow between electrodes of EEG signals.