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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Vydané v:	Sleep medicine reviews Ročník 81; s. 102097
Hlavní autori:	Kilic, Mustafa Eray, Arayici, Mehmet Emin, Turan, Oguzhan Ekrem, Yilancioglu, Yigit Resit, Ozcan, Emin Evren, Yilmaz, Mehmet Birhan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Elsevier Ltd 01.06.2025
Predmet:	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
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