Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.

Journal: Journal of Korean medical science

PMID: 33289367

Abstract

BACKGROUND: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.

Authors

Erdenebayar Urtnasan
Jong Uk Park

Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea.
Eun Yeon Joo

Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Korea.
Kyoung Joung Lee

Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea. lkj5809@yonsei.ac.kr.

Keywords

Adult Aged Deep Learning Electrocardiography Female Humans Male Middle Aged Severity of Illness Index Signal Processing, Computer-Assisted Sleep Apnea Syndromes

External Resources

View on PubMed Access via DOI PubMed (33289367)

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.

Abstract

Authors

Keywords

External Resources

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