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

Journal: IEEE journal of biomedical and health informatics
PMID: 37566509

Abstract

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 automatic semi-supervised algorithm for automated low-cost personalization fine-tuning.

Authors

  • Shuaicong Hu
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
  • Ya'nan Wang
    Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
  • Jian Liu
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
  • Cuiwei Yang
  • Aiguo Wang
    School of Computer and Information, Hefei University of Technology, Hefei, China. Electronic address: wangaiguo2546@163.com.
  • Kuanzheng Li
  • Wenxin Liu
    School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, Zhejiang P. R. China.

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