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

• URL: http://arxiv.org/abs/2404.03831v1
• Date: Thu, 4 Apr 2024 23:24:14 GMT
• Title: SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers
• Authors: Jonathan F. Carter, João Jorge, Oliver Gibson, Lionel Tarassenko,
• Abstract summary: We introduce SleepVST, a transformer model which enables state-of-the-art performance in camera-based sleep stage classification. We show that SleepVST can be successfully transferred to cardio-respiratory waveforms extracted from video, enabling fully contact-free sleep staging.
• Score: 0.6599755599064447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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