Semi-Supervised Learning for Low-cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram

Objective: Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can lead to a wide range of health issues if left untreated. This study aims to address the lack of research on personalized models for single-lead electrocardiogram (ECG)-based OSA detection, by proposing an...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 27; H. 11; S. 1 - 12
Hauptverfasser:	Hu, Shuaicong, Wang, Ya'nan, Liu, Jian, Yang, Cuiwei, Wang, Aiguo, Li, Kuanzheng, Liu, Wenxin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adaptability Algorithms Annotations Apnea Artificial neural networks Auto-encoder (AE) Biological system modeling Customization Data models Deep learning EKG Electrocardiogram (ECG) Electrocardiography Feature extraction Labels Low cost Machine learning Neural networks Obstructive Sleep Apnea (OSA) Recording Semi-supervised Semi-supervised learning Sleep apnea Sleep disorders
ISSN:	2168-2194, 2168-2208, 2168-2208
Online-Zugang:	Volltext
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