Semi-Supervised Learning for Low-Cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram-Reference-Cited by-同舟云学术

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

Published:2023-11 Issue:11 Volume:27 Page:5281-5292
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Hu Shuaicong¹^ORCID,Wang Ya'nan¹^ORCID,Liu Jian¹^ORCID,Yang Cuiwei¹^ORCID,Wang Aiguo²,Li Kuanzheng²,Liu Wenxin²

Affiliation:

1. School of Information Science and Technology, Center for Biomedical Engineering, Fudan University, Shanghai, China

2. Xinghua City People's Hospital, Jiangsu, China

Funder

Shanghai Science and Technology

Jiangsu Commission of Health

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10311342/10214652.pdf?arnumber=10214652

Reference39 articles.

1. Heart rate variability feature selection in the presence of sleep apnea: An expert system for the characterization and detection of the disorder

2. Detection of Sleep Apnea from Single-Lead ECG: Comparison of Deep Learning Algorithms

3. Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

4. Exploring the Applicability of Transfer Learning and Feature Engineering in Epilepsy Prediction Using Hybrid Transformer Model

5. Automatic Detection of Obstructive Sleep Apnea Using Wavelet Transform and Entropy-Based Features From Single-Lead ECG Signal

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