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A non-contact oxygen saturation detection method based on dynamic spectrum
Blood oxygen saturation (SpO(2)) is an important monitoring indicator for many respiratory diseases. Non-contact oximetry offers outstanding advantages in both coronavirus pandemic monitoring and sleep monitoring, but at the same time poses both challenges regarding technology and environment. There...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9598047/ https://www.ncbi.nlm.nih.gov/pubmed/36311894 http://dx.doi.org/10.1016/j.infrared.2022.104421 |
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author | Lan, Tian Li, Gang Lin, Ling |
author_facet | Lan, Tian Li, Gang Lin, Ling |
author_sort | Lan, Tian |
collection | PubMed |
description | Blood oxygen saturation (SpO(2)) is an important monitoring indicator for many respiratory diseases. Non-contact oximetry offers outstanding advantages in both coronavirus pandemic monitoring and sleep monitoring, but at the same time poses both challenges regarding technology and environment. Therefore, we propose a method for non-contact SpO(2) measurement based on the principle of DS (dynamic spectrum) in this paper. A multispectral camera with 24 wavelengths (range in 660 nm-950 nm) is used to capture video of the people's cheek region, and then the two-dimensional images are converted into a one-dimensional temporal PPG signal. After pre-processing the PPG signal, the 24 wavelengths DS values are extracted. The optimal wavelength combination is obtained by wavelength screening using the one-by-one elimination method, and a PLS (partial least squares) model is established using the SpO(2) values measured simultaneously by pulse oximetry as the modeled true values. The facial videos of eight healthy subjects were collected, and a total of 140 valid samples were obtained. By analyzing the modeling results, the regression coefficient (R) and root mean square error (RMSE) of the modeled set were 0.6366 and 0.9906, respectively. This method can significantly respond to the variation of SpO(2), and the prediction results are approaching to the prediction accuracy (±2%) of most pulse oximeters in the market. Using DS theory in this method eliminates in principle the interference of static tissue, individual differences, and environment. It fully meets the strong demand for non-contact oximetry and provides a new measurement idea. |
format | Online Article Text |
id | pubmed-9598047 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95980472022-10-26 A non-contact oxygen saturation detection method based on dynamic spectrum Lan, Tian Li, Gang Lin, Ling Infrared Phys Technol Article Blood oxygen saturation (SpO(2)) is an important monitoring indicator for many respiratory diseases. Non-contact oximetry offers outstanding advantages in both coronavirus pandemic monitoring and sleep monitoring, but at the same time poses both challenges regarding technology and environment. Therefore, we propose a method for non-contact SpO(2) measurement based on the principle of DS (dynamic spectrum) in this paper. A multispectral camera with 24 wavelengths (range in 660 nm-950 nm) is used to capture video of the people's cheek region, and then the two-dimensional images are converted into a one-dimensional temporal PPG signal. After pre-processing the PPG signal, the 24 wavelengths DS values are extracted. The optimal wavelength combination is obtained by wavelength screening using the one-by-one elimination method, and a PLS (partial least squares) model is established using the SpO(2) values measured simultaneously by pulse oximetry as the modeled true values. The facial videos of eight healthy subjects were collected, and a total of 140 valid samples were obtained. By analyzing the modeling results, the regression coefficient (R) and root mean square error (RMSE) of the modeled set were 0.6366 and 0.9906, respectively. This method can significantly respond to the variation of SpO(2), and the prediction results are approaching to the prediction accuracy (±2%) of most pulse oximeters in the market. Using DS theory in this method eliminates in principle the interference of static tissue, individual differences, and environment. It fully meets the strong demand for non-contact oximetry and provides a new measurement idea. Elsevier B.V. 2022-12 2022-10-26 /pmc/articles/PMC9598047/ /pubmed/36311894 http://dx.doi.org/10.1016/j.infrared.2022.104421 Text en © 2022 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Lan, Tian Li, Gang Lin, Ling A non-contact oxygen saturation detection method based on dynamic spectrum |
title | A non-contact oxygen saturation detection method based on dynamic spectrum |
title_full | A non-contact oxygen saturation detection method based on dynamic spectrum |
title_fullStr | A non-contact oxygen saturation detection method based on dynamic spectrum |
title_full_unstemmed | A non-contact oxygen saturation detection method based on dynamic spectrum |
title_short | A non-contact oxygen saturation detection method based on dynamic spectrum |
title_sort | non-contact oxygen saturation detection method based on dynamic spectrum |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9598047/ https://www.ncbi.nlm.nih.gov/pubmed/36311894 http://dx.doi.org/10.1016/j.infrared.2022.104421 |
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