Epilepsy detection based on spatiotemporal feature interaction fusion of EEG signals.

Objective: In recent years, with the development of machine learning and deep learning technologies, an increasing number of research works have begun using these technologies for automatic seizure detection in EEG signals. However, existing automatic seizure detection algorithms primarily focus on the features of individual EEG channels and pay less attention to the inter-channel relationships. This results in insufficient extraction of spatiotemporal information from multi-channel EEG data, affecting the final seizure detection performance. Methods: Therefore, this paper proposes an automatic seizure detection method based on the combination of Graph Attention Networks (GAT) and Transformer networks. Specifically, GAT is used as the front end for extracting spatial features, fully leveraging the topological structure of different EEG channels. Meanwhile, the Transformer network is used as the back end to explore temporal relationships and make final decisions based on the states before and after the current moment. Results: Experiments were conducted on the CHB-MIT and TUH datasets with ten-fold cross-validation. The final seizure detection accuracies on the two datasets were 98.62 and 98.12%, respectively, with the model's performance surpassing or being comparable to current state-of-the-art models. Conclusion: The proposed hybrid algorithm combines the advantages of two deep learning models, fully exploring the spatiotemporal correlations between EEG channels. Experiments on public datasets demonstrate the effectiveness of this method, significantly advancing the development of automatic seizure detection.