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Related Experiment Video

Updated: Jun 25, 2025

Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

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An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight.

Benedikt Holm1,2, Gabriel Jouan1,2, Emil Hardarson1,2

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

Frontiers in Neuroinformatics
|May 28, 2024
PubMed
Summary
This summary is machine-generated.

Integrating artificial intelligence (AI) into sleep studies significantly reduces scoring time by up to 65 minutes and improves technologist agreement. Building trust in AI algorithms is crucial for effective adoption in sleep diagnostics.

Keywords:
agreementexplainable AIhuman-in-the-loopmachine learningplatformscoring timesleep researchtrust

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Area of Science:

  • Sleep Medicine
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Polysomnography (PSG) is vital for diagnosing sleep disorders but analysis is challenging.
  • Machine learning algorithms offer automated PSG scoring, yet integration into clinical workflows is under-researched.

Purpose of the Study:

  • To present a data collection platform for harnessing multi-center PSG data.
  • To evaluate the impact of AI-enhanced PSG analysis on sleep technologists' workflow, scoring time, accuracy, and trust.

Main Methods:

  • Development of a tripartite platform: web uploader, splitter, and AI scoring processor.
  • Evaluation using real-life PSG data and human scorers, quantifying time, accuracy, and trust.
  • Qualitative interviews with scorers on platform trust and workflow integration.

Main Results:

  • AI integration decreased scoring time by up to 65 minutes.
  • AI integration increased agreement between technologists by 0.17 kappa.
  • Scorers' trust in AI algorithms was assessed.

Conclusions:

  • AI can positively impact sleep technologists' workflow by increasing speed and agreement.
  • Establishing trust in AI algorithms is essential for successful implementation in sleep diagnostics.