Using the VQ-VAE to improve the recognition of abnormalities in short-duration 12-lead electrocardiogram records.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 4, 2020

Abstract

BACKGROUND AND OBJECTIVE: Morphological diagnosis is a basic clinical task of the short-duration 12-lead electrocardiogram (ECG). Due to the scarcity of positive samples and other factors, there is currently no algorithm that is comparable to human experts in ECG morphological recognition. Our objective is to develop an ECG specialist-level deep learning method that can accurately identify ten ECG morphological abnormalities in real scene data.

Authors

  • Han Liu
    Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things, Guangdong and Hong Kong Joint Research Centre for Optical Fibre Sensors, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
  • Zhengbo Zhao
    Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Chongqing, 400010, China. Electronic address: evabobo80@163.com.
  • Xiao Chen
  • Rong Yu
    Department of Neurology, Jiulongpo District Peoples Hospital, Chongqing, 400050, China.
  • Qiang She
    Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Chongqing, 400010, China. Electronic address: qshe98@cqmu.edu.cn.

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