A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Journal: Medical & biological engineering & computing
PMID: 34677739

Abstract

Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019. This dataset covers most types of ECG interpretations, including the normal type, 8 common abnormal types, and the other type which includes both uncommon abnormal and noise signals. Based on the Contest, we systematically assessed and analyzed a set of top-performing methods, most of which are deep neural networks, with both their commonalities and characteristics. This study establishes the benchmarks for computerized interpretation of 12-lead resting electrocardiogram and provides insights for the development of new methods. Graphical Abstract A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Authors

  • Zijian Ding
    Department of Electronic Engineering, Tsinghua University, Beijing, China.
  • GuiJin Wang
  • HuaZhong Yang
  • Ping Zhang
    Department of Computer Science and Engineering, The Ohio State University, USA.
  • Dapeng Fu
    Chinese Academy of Sciences Zhong Guan Cun Hospital, Beijing, China.
  • Zhen Yang
    CAS Max-Planck Partner Institute for Computational Biology, Shanghai Institute of Biological Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
  • Xinkang Wang
    ECG Diagnosis Department, Fujian Provincial Hospital, Fuzhou, China.
  • Xia Wang
    Department of Neurology, The Sixth People's Hospital of Huizhou City, Huizhou, China.
  • Zhourui Xia
    Tsinghua-Berkerley Shenzhen Institute, Shenzhen, China.
  • Chiming Zhang
    Southwest University of Science and Technology, Mianyang, China.
  • Wenjie Cai
    University of Shanghai for Science and Technology, Shanghai, China.
  • Binhang Yuan
    Rice University, Houston, USA.
  • Dongya Jia
  • Bo Chen
  • Chengbin Huang
    East China Normal University, Shanghai, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Yi Li
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Shan Yang
    Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.
  • Runnan He
    Harbin Institute of Technology, Harbin, China.

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