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Tytuł:
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Improving Machine Learning Technology in the Field of Sleep.
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Autorzy:
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Mallett J; Department of Computer Science, Reykjavik University, Menntavegur 1, Reykjavik 102, Iceland. Electronic address: .
Arnardottir ES; Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland; Internal Medicine Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland.
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Źródło:
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Sleep medicine clinics [Sleep Med Clin] 2021 Dec; Vol. 16 (4), pp. 557-566. Date of Electronic Publication: 2021 Oct 06.
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Typ publikacji:
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Journal Article; Review
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Język:
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English
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Imprint Name(s):
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Original Publication: New York, N.Y. : Elsevier
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MeSH Terms:
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Sleep Apnea, Obstructive*/diagnosis
Sleep Apnea, Obstructive*/therapy
Humans ; Machine Learning ; Polysomnography ; Sleep ; Technology
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Contributed Indexing:
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Keywords: Deep learning; Polysomnography; Sleep apnea; Sleep staging
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Entry Date(s):
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Date Created: 20211029 Date Completed: 20211125 Latest Revision: 20211125
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Update Code:
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20240105
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DOI:
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10.1016/j.jsmc.2021.08.003
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PMID:
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34711381
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The authors discuss the challenges of machine- and deep learning-based automatic analysis of obstructive sleep apnea with respect to known issues with the signal interpretation, patient physiology, and the apnea-hypopnea index. Their goal is to provide guidance for sleep and machine learning professionals working in this area of sleep medicine. They suggest that machine learning approaches may well be better targeted at examining and attempting to improve the diagnostic criteria, in order to build a more nuanced understanding of the detailed circumstances surrounding OSA, rather than merely attempting to reproduce human scoring.
Competing Interests: Disclosure No relevant conflict of interest to disclose.
(Copyright © 2021 Elsevier Inc. All rights reserved.)
