Seizure detection by brain-connectivity analysis using dynamic graph isomorphism network

Epilepsy is a neurological disease caused by ab-normal neural electrical discharges. Electroencephalography (EEG) is a powerful tool to measure the brain electrical activity and has been widely used for seizure detection. Manual EEG analysis is labor-intensive and time-consuming. Automatic seizure detection is urgently demanded for long-time seizure monitoring. Many methods have been proposed for automatic seizure detection based on EEG signals. However, most of the existing methods are patient-specific with limited generaliz-ability. Few studies investigate inter-patient seizure detection, which remains challenging. The aim of the present study is therefore to develop advanced algorithms for efficient inter-patient seizure detection using EEG. To this end, dynamic brain network is employed to capture the spatiotemporal dynamics of the connectivity among brain regions. A novel graph neural network referred to as graph isomorphic network is proposed for effective local-global spatiotemporal feature extraction and seizure classification. The proposed method is evaluated with the CHB-MIT open dataset with a ten-fold cross-validation. The results reveal excellent performance for the proposed method, with accuracy, sensitivity, and specificity of 96.2%, 95.4%, and 97.0% respectively, significantly higher than the results reported in the literature. Our results provide useful information for inter-patient seizure detection, particularly for long-time ambulatory seizure monitoring.