SleepVST: Sleep Staging from Near-Infrared Video Signals Using Pre-Trained Transformers

Abstract

Advances in camera-based physiological monitoring have enabled the robust non-contact measurement of respiration and the cardiac pulse which are known to be indicative of the sleep stage. This has led to research into camera-based sleep monitoring as a promising alternative to "gold-standard" polysomnography which is cumbersome expensive to administer and hence unsuitable for longer-term clinical studies. In this paper we introduce SleepVST a transformer model which enables state-of-the-art performance in camera-based sleep stage classification (sleep staging). After pre-training on contact sensor data SleepVST outperforms existing methods for cardio-respiratory sleep staging on the SHHS and MESA datasets achieving total Cohen's kappa scores of 0.75 and 0.77 respectively. We then show that SleepVST can be successfully transferred to cardio-respiratory waveforms extracted from video enabling fully contact-free sleep staging. Using a video dataset of 50 nights we achieve a total accuracy of 78.8% and a Cohen's \kappa of 0.71 in four-class video-based sleep staging setting a new state-of-the-art in the domain.