A Two-Stage Prediction + Detection Framework for Real-Time Epileptic Seizure Monitoring

Wearable intelligent devices are essential for the health monitoring of epilepsy patients in non-hospital environment. The existing seizure detection algorithms for wearable health monitoring devices cannot achieve high sensitivity, short detection latency, low false alarm rate (FAR), as well as lightweight computing simultaneously. In this paper, we propose a two-stage prediction + detection framework, PDNet, for real-time epileptic seizure monitoring. The proposed two-stage PDNet framework consists of a lightweight seizure predictor and a highly-accurate seizure detector. Only when the first-stage seizure predictor forecasts an impending seizure, the second-stage seizure detector is activated to precisely and rapidly classify the seizure states, thereby significantly reducing the amount of computations. A semi-supervised learning strategy is employed to enhance the decision boundary of the seizure predictor, which is used for EEG pre-processing instead of prediction only. Soft labels are adopted to enable the seizure detector to precisely distinguish the seizure states. The proposed PDNet is evaluated using the CHB-MIT scalp EEG database. The proposed PDNet achieves 99.0% sensitivity, 0.43/h FAR, and 3.52-second detection latency with 3.02M multiply–accumulate (MAC) operations, which are competitive compared to the state of the art in terms of sensitivity, detection latency, FAR, and computation complexity. Furthermore, the fine-grained information such as the occurrence process of seizures demonstrated by soft labels can help the caregivers or clinicians to come up with targeted healthcare and clinical treatments.