A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification.

Journal: Computer methods and programs in biomedicine
Published Date: Mar 23, 2022

Abstract

BACKGROUND AND OBJECTIVE: The automatic recognition of myocardial infarction (MI) by artificial intelligence (AI) has been an emerging topic of academic research and an existing classification method that can recognize conventional electrocardiogram (ECG) signals with high accuracy. However, they are employed to classify one-dimensional (1-D) ECG signals rather than three-dimensional (3-D) ECG images, and it is limited to provide physicians with significant recommendations to aid in diagnosis like highlighting abnormal leads. Other studies on 3-D ECG images either did not achieve high accuracy or did not employ an inter-patient classification scheme. By proposing a multi-VGG deep neural network, this study aims to develop an automatic classification method for identifying myocardial infarction with inter-patient high accuracy and proper interpretability using 3-D ECG image and a Grad-CAM++ method.

Authors

  • Rui Fang
    Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan.
  • Chih-Cheng Lu
    Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Intelligent Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan. Electronic address: cclu23@mail.ntut.edu.tw.
  • Cheng-Ta Chuang
    Department of Intelligent Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
  • Wen-Han Chang
    Emergency Department, Mackay Memorial Hospital, Taipei 10449, Taiwan.

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