EEG-based Seizure Type Classification with Temporal-Spatial-Spectral Attention

Seizure detection and type classification from electroencephalogram (EEG) has the potential to improve the diagnosis and treatment of epilepsy. Although the task of seizure detection has been well-investigated, seizure type classification remains largely unexplored. The intricate nature of seizure dynamics presents a significant challenge in effectively extracting distinguishing features from noisy and high-dimensional EEG signals. Previous studies mainly focus on extracting features from temporal, spectral or special domain. Extracting temporal-spatial-spectral features simultaneously from EEG remains challenging. In this paper, we introduce an attention-based neural network to effectively extract temporal-spatial-spectral EEG features, for seizure type classification. It utilizes an attention module with one-shot aggregation to extract multi-level temporal-spatial-spectral EEG features, aiming to differentiate the complex patterns of various seizure types. Specifically, we construct the 3-dimensional representation of EEG by stacking the time-frequency matrices obtained from short time Fourier transform. The attention module consists of paralleled temporal and spatial-spectral attention blocks, allowing the model to focus on the most distinctive time stamps, sensor locations, and frequency bands. The proposed approach is validated on the largest public seizure EEG database, TUSZ v1.5.2. Five-fold cross validation demonstrates that our framework achieved 0.951 weighted F1 score on seizure type classification, achieving state-of-the-art performance. Ablation study confirmed the effectiveness of the temporal and spatial-spectral attention blocks.