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Updated: Sep 13, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
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Unsupervised machine learning in sleep research: a scoping review.

Luka Biedebach1,2, Daniela Ferreira-Santos3,4, Marie-Ange Stefanos5

  • 1Department of Computer Science, Reykjavik University, Reykjavik, Iceland.

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Unsupervised machine learning is increasingly used in sleep research, with clustering being a common method. This review maps current applications and identifies future research directions in sleep studies.

Keywords:
scoping reviewsleepunsupervised machine learning

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Area of Science:

  • Sleep Science
  • Artificial Intelligence
  • Data Mining

Background:

  • Unsupervised machine learning (ML) excels at pattern discovery in data without labels.
  • Its success in sleep research highlights broader potential for novel insights.
  • Further exploration is warranted for advanced sleep studies.

Purpose of the Study:

  • To conduct a scoping review of unsupervised ML in sleep research.
  • To map existing literature and identify research gaps.
  • To propose future research directions.

Main Methods:

  • Scoping review following PRISMA guidelines.
  • Comprehensive literature search yielding 3960 publications.
  • Full-text review of 356 selected publications.

Main Results:

  • Significant increase in publications over the last decade.
  • Clustering is the predominant unsupervised ML method.
  • Diverse data sources used, including wearables, video, audio, and medical imaging.
  • Applications span general and clinical sleep populations.

Conclusions:

  • The review provides a comprehensive overview of unsupervised ML in sleep.
  • Identified gaps in the literature for future investigation.
  • Outlined key directions for advancing sleep research using unsupervised ML.