Video-PSG: An Intelligent Contactless Monitoring System for Sleep Staging.

Abstract

Polysomnography (PSG) is the gold standard for sleep staging in clinics, but its skin-contact nature makes it uncomfortable and inconvenient to use for long-term sleep monitoring. As a complementary part of PSG, the video cameras are not utilized to their full potential, only for manual check of simple sleep events, thereby ignoring the potential for physiological and semantic measurement. This leads to a pivotal research question: Can camera be used for sleep staging, and to what extent? We developed a camera-based contactless sleep staging system in the Institute of Respiratory Diseases and created a clinical video dataset of 20 adults. The camera-based feature set, derived from both physiological signals (pulse and breath) and motions all measured from a video, was evaluated for 4-class sleep staging (Wake-REM-Light-Deep). Three optimization strategies were proposed to enhance the sleep staging accuracy: using motion metrics to prune measurement outliers, creating a more personalized model based on the baseline calibration of waking-stage physiological signals, and deriving a specialized feature for REM detection. It achieved the best accuracy of 73.1% (kappa = 0.62, F1-score = 0.75) in the benchmark of five sleep-staging classifiers. Notably, the system exhibited high accuracy in predicting the overall sleep structure and subtle changes between different sleep stages. The study demonstrates that camera-based contactless sleep staging is a new value stream for sleep medicine, which also provides clinical and technical insights for future optimization and implementation.