Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms.

Journal: The Canadian journal of cardiology
PMID: 32585216

Abstract

BACKGROUND: Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias with the use of a single-lead ECG input data set have been developed. It remains to be determined whether these algorithms can be generalized to 12-lead ECG-based rhythm classification.

Authors

  • Kuan-Cheng Chang
    Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. Electronic address: kuancheng.chang@gmail.com.
  • Po-Hsin Hsieh
    Department of Biomedical Imaging and Radiologic Science, China Medical University, Taichung, Taiwan.
  • Mei-Yao Wu
    School of Postbaccalaureate Chinese Medicine, China Medical University, Taichung, Taiwan; Department of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan.
  • Yu-Chen Wang
    Department of Orthodontics, General Hospital of Chinese PLA. Beijing 100853, China. E-mail:andychen19871014@126.com.
  • Jan-Yow Chen
    Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.
  • Fuu-Jen Tsai
    Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
  • Edward S C Shih
    Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
  • Ming-Jing Hwang
    Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
  • Tzung-Chi Huang
    Department of Biomedical Imaging and Radiologic Science, China Medical University, Taichung, Taiwan; Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.

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