AI-Enhanced Biosignal Analysis for Obstructive Sleep Apnea Detection: A Comprehensive Review

Aida Noor Indrawati; Nuryani Nuryani; Wiharto; Diah Kurnia Mirawati

doi:10.3991/ijoe.v20i14.50175

AI-Enhanced Biosignal Analysis for Obstructive Sleep Apnea Detection: A Comprehensive Review

Authors

Aida Noor Indrawati Medical Instrumentation, Department of Physics, Sebelas Maret University, Surakarta, Indonesia
Nuryani Nuryani Department of Informatics, Sebelas Maret University, Surakarta, Indonesia https://orcid.org/0000-0001-8126-3205
Wiharto Department of Informatic, Sebelas Maret University, Surakarta, Indonesia
Diah Kurnia Mirawati Medical Sciences Department, Faculty of Medicine, Sebelas Maret University, Surakarta, Indonesia https://orcid.org/0000-0002-0558-5680

DOI:

https://doi.org/10.3991/ijoe.v20i14.50175

Keywords:

, Obstructive sleep apnea (OSA), Algorithm in detection, Artificial Inteliigence

Abstract

Obstructive sleep apnea (OSA) detection using single-lead electrocardiograms (ECGs) has advanced significantly with the integration of artificial intelligence (AI). This review explores how AI enhances feature extraction and machine learning algorithms to improve OSA detection. The RR interval in electrocardiographic data is particularly valued for its ease of identification and low error rate. We review a range of machine learning and deep learning techniques employed in OSA detection. This review offers insights into developing single-lead ECG-based OSA detection systems by analyzing database availability, feature extraction methods, and machine learning approaches.

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Published

2024-11-14

How to Cite

Indrawati, A. N., Nuryani, N., Wiharto, & Mirawati, D. K. (2024). AI-Enhanced Biosignal Analysis for Obstructive Sleep Apnea Detection: A Comprehensive Review. International Journal of Online and Biomedical Engineering (iJOE), 20(14), pp. 139–159. https://doi.org/10.3991/ijoe.v20i14.50175