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

Source: [SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers](<https://www.sota2.com/research/paper/68fa5f571e209131ce9e913a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:42:52.293Z

Authors: Jonathan F. Carter, Jo\\~ao Jorge, Oliver Gibson, Lionel Tarassenko

Published: 2024-04-04T23:24:14.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Four-Class Cardio-Respiratory Sleep Staging on MESA](<https://www.sota2.com/research/sota/four-class-cardio-respiratory-sleep-staging-on-mesa.md>): four-class cardio-respiratory sleep staging; MESA; Kappa\_mu: 0.76.

- [Four-Class Cardio-Respiratory Sleep Staging on SHHS](<https://www.sota2.com/research/sota/four-class-cardio-respiratory-sleep-staging-on-shhs.md>): four-class cardio-respiratory sleep staging; SHHS; Kappa\_Mu: 0.73.

- [Four-class sleep staging on Oxford Sleep Volunteers](<https://www.sota2.com/research/sota/four-class-sleep-staging-on-oxford-sleep-volunteers.md>): four-class sleep staging; Oxford Sleep Volunteers; Kappa (mu): 68.

- [Four-Class Sleep Staging on Healthy infants](<https://www.sota2.com/research/sota/four-class-sleep-staging-on-healthy-infants.md>): four-class sleep staging; Healthy infants; mu-Kappa: not recorded.

- [Four-Class Sleep Staging on Healthy adults](<https://www.sota2.com/research/sota/four-class-sleep-staging-on-healthy-adults.md>): four-class sleep staging; Healthy adults; Kappa (mu): not recorded.

- [Four-Class Sleep Staging on HealthBed Study](<https://www.sota2.com/research/sota/four-class-sleep-staging-on-healthbed-study.md>): four-class sleep staging; HealthBed Study; Kappa (mu): not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.03831>)

[PDF](<https://arxiv.org/pdf/2404.03831.pdf>)
