End-to-end model for automatic seizure detection using supervised contrastive learning

Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures, caused by abnormal electrical activity in cerebral neurons. Given that it is one of the most common neurological disorders globally, the efficient and accurate automatic seizure detection is urgently needed in the diagnosis of epilepsy to reduce the workload of continuous electroencephalogram (EEG) monitoring. Current deep learning based seizure detection approaches usually employ cross-entropy loss as objective function, which generally suffer from inadequate utilization of sample labels and poor classification margins, resulting in decreased performance in seizure detection. In this study, we propose an end-to-end automatic seizure detection framework based on supervised contrastive learning, which effectively utilizes labeled EEG to cluster similar samples while separating dissimilar ones. A supervised contrastive learning loss is employed to optimize classification boundaries by making full use of EEG labels. We employ long-term continuous EEG for evaluation. Given the presence of various noise and interferences, assessment on long-term continuous EEG proves to be more challenging. Post-processing techniques such as smoothing filter, threshold judgment, and collar technique are further adopted to diminish the artifact impacts on seizure detection performance. The proposed method is evaluated on the publicly available Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, achieving an event-based sensitivity of 99.71% and a false detection rate (FDR) of 0.35/h.