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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| Vydáno v: | Sleep medicine reviews Ročník 81; s. 102097 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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England
Elsevier Ltd
01.06.2025
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| ISSN: | 1087-0792, 1532-2955, 1532-2955 |
| On-line přístup: | Získat plný text |
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| Abstract | 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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| AbstractList | 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. AbstractSleep 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. 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.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. |
| ArticleNumber | 102097 |
| Author | Yilancioglu, Yigit Resit Arayici, Mehmet Emin Turan, Oguzhan Ekrem Ozcan, Emin Evren Kilic, Mustafa Eray Yilmaz, Mehmet Birhan |
| Author_xml | – sequence: 1 givenname: Mustafa Eray orcidid: 0000-0002-0894-8790 surname: Kilic fullname: Kilic, Mustafa Eray email: mustafaeraykilic@gmail.com organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 2 givenname: Mehmet Emin orcidid: 0000-0002-0492-5129 surname: Arayici fullname: Arayici, Mehmet Emin email: mehmetearayici@gmail.com organization: Department of Biostatistics and Medical Informatics, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 3 givenname: Oguzhan Ekrem orcidid: 0000-0003-3557-1682 surname: Turan fullname: Turan, Oguzhan Ekrem email: oguzhanekremturan@deu.edu.tr organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 4 givenname: Yigit Resit orcidid: 0000-0001-5762-1383 surname: Yilancioglu fullname: Yilancioglu, Yigit Resit email: yigityilancioglu@deu.edu.tr organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 5 givenname: Emin Evren orcidid: 0000-0002-2198-9300 surname: Ozcan fullname: Ozcan, Emin Evren email: eevrenozcan@deu.edu.tr organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 6 givenname: Mehmet Birhan orcidid: 0000-0002-8169-8628 surname: Yilmaz fullname: Yilmaz, Mehmet Birhan email: profdrmbyilmaz@gmail.com organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye |
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| Keywords | Deep learning Single-lead ECG Diagnostic accuracy Machine learning Sensitivity and specificity Sleep apnea Electrocardiogram (ECG) Sleep medicine Meta-analysis |
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| Snippet | Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG),... AbstractSleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography... Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG),... |
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| SubjectTerms | 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 |
| Title | Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis |
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