*Result*: Unsupervised machine learning in sleep research: a scoping review.
Title:
Unsupervised machine learning in sleep research: a scoping review.
Authors:
Biedebach L; Department of Computer Science, Reykjavik University, Reykjavik, Iceland.; Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland., Ferreira-Santos D; Department of Medical Physics, University of Eastern Finland, Kuopio, Finland.; INESC TEC, Universidade do Porto, Porto, Portugal., Stefanos MA; Computer Science Department, Université Paris-Cité, Paris, France., Lindhagen A; Department of Computer Science, Umeå University, Umeå, Sweden., Pires GN; Departmento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil.; Hospital Israelita Albert Einstein - Sao Paulo, SP, Brazil.; Faculdade Israelita de Ciencias da Saude Albert Einstein - Sao Paulo, SP, Brazil., Arnardóttir ES; Department of Computer Science, Reykjavik University, Reykjavik, Iceland.; Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland., Islind AS; Department of Computer Science, Reykjavik University, Reykjavik, Iceland.
Source:
Sleep [Sleep] 2025 Nov 10; Vol. 48 (11).
Publication Type:
Journal Article; Scoping Review
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: United States NLM ID: 7809084 Publication Model: Print Cited Medium: Internet ISSN: 1550-9109 (Electronic) Linking ISSN: 01618105 NLM ISO Abbreviation: Sleep Subsets: MEDLINE
Imprint Name(s):
Publication: 2017- : New York : Oxford University Press
Original Publication: New York, Raven Press.
Original Publication: New York, Raven Press.
MeSH Terms:
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Front Hum Neurosci. 2021 Oct 08;15:727139. (PMID: 34690720)
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J Neurosci Methods. 2015 Jul 30;250:94-105. (PMID: 25629798)
Neurosci Conscious. 2020 Jul 15;2020(1):niaa006. (PMID: 32695475)
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J Neural Eng. 2020 Jun 25;17(3):036031. (PMID: 32454463)
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Contributed Indexing:
Keywords: scoping review; sleep; unsupervised machine learning
Entry Date(s):
Date Created: 20250728 Date Completed: 20251112 Latest Revision: 20251205
Update Code:
20260130
PubMed Central ID:
PMC12597679
DOI:
10.1093/sleep/zsaf189
PMID:
40719375
Database:
MEDLINE
*Further Information*
*Study Objectives: Unsupervised machine learning-an approach that identifies patterns and structures within data without relying on labels-has demonstrated remarkable success in various domains of sleep research. This underscores the broader utility of machine learning, suggesting that its capabilities extend beyond current applications and warrant further exploration for novel insights in sleep studies, focusing specifically on unsupervised machine learning.
Methods: This paper outlines a scoping review conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for scoping reviews. A comprehensive search covering various search terms focusing on the intersection between unsupervised machine learning and sleep led to 3960 publications. After screening all titles and abstracts with two independent reviewers, ultimately, 356 publications were included in the full-text review. The data extracted from the full texts included information about the machine learning methods and types of sleep data, as well as the study population.
Results: There has been a steep increase in the number of publications in this research area in the past 10 years. Clustering is the most commonly used method, but other methods are gaining popularity. Apart from classical polysomnography, data from wearable devices, nearables, video, audio, and medical imaging techniques have been used as input to unsupervised machine learning. The broad search allowed us to explore various applications within sleep research, ranging from the general population to populations with various sleep disorders.
Conclusion: The review mapped existing research on unsupervised learning in sleep research, identified gaps in the literature, and derived directions for future research.
(© The Author(s) 2025. Published by Oxford University Press on behalf of Sleep Research Society.)*