Automated seizure detection in epilepsy using a novel dynamic temporal-spatial graph attention network
Kunxian Yan, Xiangyu Luo, Lei Ye, Wenping Geng, Jian He, Jiliang Mu, Xiaojuan Hou, Xiang Zan, Jiuhong Ma, Fei Li, Le Zhang, Xiujian Chou
Abstract
Epilepsy is a neurological disorder characterized by recurrent seizures caused by excessive electrical discharges in brain cells, posing significant diagnostic and therapeutic challenges. Dynamic brain network analysis via electroencephalography (EEG) has emerged as a powerful tool for capturing transient functional connectivity changes, offering advantages over static networks. In this study, we propose a Dynamic Temporal-Spatial Graph Attention Network (DTS-GAN) to address the limitations of fixed-topology graph models in analysing time-varying brain networks. By integrating graph signal processing with a hybrid deep learning framework, DTS-GAN collaboratively extracts spatiotemporal features through two key modules: an LSTM-based temporal encoder to model long-term dependencies in EEG sequences, and a dynamic graph attention network with probabilistic Gaussian connectivity, enabling adaptive learning of transient functional interactions across electrode nodes. Experiments on the TUSZ dataset demonstrate that DTS-GAN achieves 89-91% accuracy and a weighted F1-score of 87-91% in classifying seven seizure types, significantly outperforming baseline models. The multi-head attention mechanism and dynamic graph generation strategy effectively resolve the temporal variability of functional connectivity. These results highlight the potential of DTS-GAN in providing precise and automated seizure detection, serving as a robust tool for clinical EEG analysis.