SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914741993078784 |
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| author | Carter, Jonathan F. Jorge, João Gibson, Oliver Tarassenko, Lionel |
| author_facet | Carter, Jonathan F. Jorge, João Gibson, Oliver Tarassenko, Lionel |
| contents | 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 $κ$ of 0.71 in four-class video-based sleep staging, setting a new state-of-the-art in the domain. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_03831 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers Carter, Jonathan F. Jorge, João Gibson, Oliver Tarassenko, Lionel Computer Vision and Pattern Recognition Human-Computer Interaction Neurons and Cognition 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 $κ$ of 0.71 in four-class video-based sleep staging, setting a new state-of-the-art in the domain. |
| title | SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction Neurons and Cognition |
| url | https://arxiv.org/abs/2404.03831 |