Automated seizure detection in epilepsy using a novel dynamic temporal-spatial graph attention network

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.