Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 3, 2024

Abstract

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular diseases (CVDs). It is important to consider both temporal and spatial features in ECG analysis to improve automated CVDs diagnosis. Significant progress has been made in automated CVDs diagnosis based on ECG with the continuous development of deep learning. Current most researches often treat 12-lead ECG signals as synchronous sequences in Euclidean space, focusing primarily on extracting temporal features while overlooking the spatial relationships among the 12-lead. However, the spatial distribution of 12-lead ECG electrodes can be more naturally represented using non-Euclidean data structures, which makes the relationships among leads more consistent with their intrinsic characteristics.

Authors

  • Yupeng Qiang
    South China University of Technology, Guangzhou, 510641, China. Electronic address: auqyp@mail.scut.edu.cn.
  • Xunde Dong
    Center for Control and Optimization, School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
  • Xiuling Liu
    Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Yihai Fang
    Department of Civil Engineering, Monash University, Clayton, 3800, Victoria, Australia.
  • Jianhong Dou
    General Hospital of the Southern Theater of the Chinese People's Liberation Army, Guangzhou, 510030, China. Electronic address: doujianhong@hotmail.com.

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