Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis

Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG), the current diagnostic gold standard. Single-lead electrocardiography (ECG) has been proposed as a potential alternative diagnostic tool, but...

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Published in:Sleep medicine reviews Vol. 81; p. 102097
Main Authors: Kilic, Mustafa Eray, Arayici, Mehmet Emin, Turan, Oguzhan Ekrem, Yilancioglu, Yigit Resit, Ozcan, Emin Evren, Yilmaz, Mehmet Birhan
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.06.2025
Subjects:
Algorithms
Deep Learning
Diagnostic accuracy
Electrocardiogram (ECG)
Electrocardiography - methods
Humans
Machine learning
Machine Learning - standards
Meta-analysis
Neurology
Polysomnography - methods
Sensitivity and Specificity
Single-lead ECG
Sleep apnea
Sleep Apnea Syndromes - diagnosis
Sleep Medicine
Deep learning
Single-lead ECG
Diagnostic accuracy
Machine learning
Sensitivity and specificity
Sleep apnea
Electrocardiogram (ECG)
Sleep medicine
Meta-analysis
ISSN:1087-0792, 1532-2955, 1532-2955
Online Access:Get full text
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Summary:Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG), the current diagnostic gold standard. Single-lead electrocardiography (ECG) has been proposed as a potential alternative diagnostic tool, but interpretation challenges remain. Recent advances in machine learning and deep learning technologies offer promising approaches for enhancing the detection of sleep apnea through automated analysis of ECG signals. This meta-analysis aims to evaluate the diagnostic accuracy of machine learning (ML) and deep learning (DL) algorithms in detecting sleep apnea patterns from single-lead ECG data. A comprehensive literature search across multiple databases was conducted through November 2023, adhering to PRISMA-DTA guidelines. Studies that included sensitivity and specificity data for ECG-based sleep apnea detection using (machine learning/deep learning) ML/DL were selected. The analysis included 84 studies, demonstrating high diagnostic accuracy for ML/DL algorithms, with pooled sensitivity and specificity of over 90 % in per-segment analysis and close to 97 % in per-record analysis. Despite strong diagnostic performance, variations in algorithm effectiveness and methodological biases were noted. This meta-analysis highlights the potential of ML and DL in improving sleep apnea diagnosis and outlines areas for future research to address current limitations.
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ISSN:1087-0792
1532-2955
1532-2955
DOI:10.1016/j.smrv.2025.102097