Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
12 08 2023
12 08 2023
Historique:
received:
01
02
2023
accepted:
03
08
2023
medline:
14
8
2023
pubmed:
13
8
2023
entrez:
12
8
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37573327
doi: 10.1038/s41467-023-40604-3
pii: 10.1038/s41467-023-40604-3
pmc: PMC10423260
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4881Informations de copyright
© 2023. Springer Nature Limited.
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