Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry

Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordin...

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Autores principales: Levy, Jeremy, Álvarez, Daniel, Del Campo, Félix, Behar, Joachim A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423260/
https://www.ncbi.nlm.nih.gov/pubmed/37573327
http://dx.doi.org/10.1038/s41467-023-40604-3
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author Levy, Jeremy
Álvarez, Daniel
Del Campo, Félix
Behar, Joachim A.
author_facet Levy, Jeremy
Álvarez, Daniel
Del Campo, Félix
Behar, Joachim A.
author_sort Levy, Jeremy
collection PubMed
description Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, called OxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-to-severe OSA patients against 21% for the best benchmark.
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spelling pubmed-104232602023-08-14 Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry Levy, Jeremy Álvarez, Daniel Del Campo, Félix Behar, Joachim A. Nat Commun Article Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, called OxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-to-severe OSA patients against 21% for the best benchmark. Nature Publishing Group UK 2023-08-12 /pmc/articles/PMC10423260/ /pubmed/37573327 http://dx.doi.org/10.1038/s41467-023-40604-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Levy, Jeremy
Álvarez, Daniel
Del Campo, Félix
Behar, Joachim A.
Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry
title Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry
title_full Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry
title_fullStr Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry
title_full_unstemmed Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry
title_short Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry
title_sort deep learning for obstructive sleep apnea diagnosis based on single channel oximetry
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423260/
https://www.ncbi.nlm.nih.gov/pubmed/37573327
http://dx.doi.org/10.1038/s41467-023-40604-3
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