Causal Representation Learning for Cross-Patient Seizure Classification

Automatic seizure type classification from electroencephalogram (EEG) data can help clinicians to better diagnose epilepsy. Although many previous studies have focused on the classification problem of seizure EEG data, most of these methods require that there is no distribution shift between training data and test data, which greatly limits the applicability in real-world scenarios. In this paper, we propose an invariant spatiotemporal representation learning method for cross-patient seizure classification. Specifically, we first split the spatiotemporal EEG data into different environments based on heterogeneous risk minimization to reflect the spurious correlations. We then learn invariant spatiotemporal representations and train the seizure classification model based on the learned representations to achieve accurate seizure-type classification across various environments. The experiments are conducted on the largest public EEG dataset, the Temple University Hospital Seizure Corpus (TUSZ) dataset, and the experimental results demonstrate the effectiveness of our method.