Automatic seizure detection from electroencephalography (EEG) based on deep learning has been significantly improved. However, existing works have not adequately excavate the spatial-temporal information between EEG channels. Besides, most works mainly focus on patient-specific scenarios while cross-patient seizure detection is more challenging and meaningful. Regarding the above problems, we propose a hybrid attention network (HAN) for automatic seizure detection. Specifically, the graph attention network (GAT) extracts spatial features at the front end, and Transformer gets time features as the back end. HAN leverages the attention mechanism and fully extracts the spatial-temporal correlation of EEG signals. The focal loss function is introduced to HAN to deal with the imbalance of the dataset accompanied by seizure detection based on EEG. Both patient-specific and patient-independent experiments are carried out on the public CHB-MIT database. Experimental results demonstrate the efficacy of HAN in both experimental settings.
Keywords: EEG; Seizure detection; focal loss; graph attention network; transformer.