Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study.

Journal: The Lancet. Digital health
Published Date: Jun 4, 2020

Abstract

BACKGROUND: Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis.

Authors

  • Hongling Zhu
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Cheng Cheng
    School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Hang Yin
    Department of Gastroenterology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200011, China.
  • Xingyi Li
    School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Ping Zuo
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Jia Ding
    Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China.
  • Fan Lin
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jingyi Wang
  • Beitong Zhou
    School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Yonge Li
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Shouxing Hu
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Yulong Xiong
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Binran Wang
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Guohua Wan
    Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China.
  • Xiaoyun Yang
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Ye Yuan
    School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan 430074, China.

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