A Hybrid Anomaly Detection Framework for Reliable Physiological Signal Extraction in Multimodal Wearable Sleep Monitoring
Wearable sleep monitoring systems provide a scalable and low-burden alternative to laboratory-based polysomnography, but overnight physiological recordings collected from wearable sensors are frequently corrupted by poor skin contact, sensor displacement, flatline behavior, saturation, abrupt autoscaling, outliers, and...