SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers
<|reference_start|>SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers: 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.<|reference_end|>
@article{carter2024sleepvst:,
    title={SleepVST: Sleep Staging from Near-Infrared Video Signals using
  Pre-Trained Transformers},
    author={Jonathan F. Carter, Jo~ao Jorge, Oliver Gibson, Lionel Tarassenko},
    journal={arXiv preprint arXiv:2404.03831},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03831},
    primaryClass={cs.CV cs.HC q-bio.NC}
}