Enhancing automatic multilabel diagnosis of electrocardiogram signals: A masked transformer approach.

Journal: Computers in biology and medicine
Published Date: Jul 7, 2025

Abstract

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. Although deep learning models have been widely applied to ECG classification tasks, their accuracy remains limited, especially in handling complex signal patterns in real-world clinical settings. This study explores the potential of Transformer models to improve ECG classification accuracy.

Authors

  • Ya Zhou
    School of Marxism, Southeast University, Nanjing, Jiangsu 211189, China.
  • Xiaolin Diao
    Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China.
  • Yanni Huo
    Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Zhaohong Sun
    Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
  • Xiaohan Fan
    Function Test Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China; Cardiac Arrhythmia Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China. Electronic address: fanxiaohan@fuwaihospital.org.
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.

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