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 nonhospital environments. 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 article, 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 semisupervised learning strategy is employed to enhance the decision boundary of the seizure predictor, which is used for electroencephalogram (EEG) preprocessing 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-s detection latency with 3.02 M 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, 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.