Fully-Automatic Camera-Based Pulse-Oximetry During Sleep
Abstract
Current routines for the monitoring of sleep require many sensors attached to the patient during a nocturnal observational study, limiting mobility and causing stress and discomfort. Cameras have shown promise in the remote monitoring of pulse rate, respiration and oxygen saturation, which potentially allows a reduction in the number of sensors. Applying these techniques in a sleep setting is challenging, as it is unknown upfront which portion of the skin will be visible, there is no unique skin-color outside the visible range, and the pulsatility is low in infrared. We present a fully-automatic living tissue detection method to enable continuous monitoring of pulse rate and oxygen saturation during sleep. The system is validated on a dataset where various typical sleep scenarios have been simulated. Results show the proposed method to outperform the current state-of-the-art, especially for the estimation of oxygen saturation.
Cite
Text
Vogels et al. "Fully-Automatic Camera-Based Pulse-Oximetry During Sleep." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00183Markdown
[Vogels et al. "Fully-Automatic Camera-Based Pulse-Oximetry During Sleep." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/vogels2018cvprw-fullyautomatic/) doi:10.1109/CVPRW.2018.00183BibTeX
@inproceedings{vogels2018cvprw-fullyautomatic,
title = {{Fully-Automatic Camera-Based Pulse-Oximetry During Sleep}},
author = {Vogels, Tom and van Gastel, Mark and Wang, Wenjin and de Haan, Gerard},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2018},
pages = {1349-1357},
doi = {10.1109/CVPRW.2018.00183},
url = {https://mlanthology.org/cvprw/2018/vogels2018cvprw-fullyautomatic/}
}