MA-MIL: Sampling point-level abnormal ECG location method via weakly supervised learning.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 9, 2024

Abstract

BACKGROUND AND OBJECTIVE: Current automatic electrocardiogram (ECG) diagnostic systems could provide classification outcomes but often lack explanations for these results. This limitation hampers their application in clinical diagnoses. Previous supervised learning could not highlight abnormal segmentation output accurately enough for clinical application without manual labeling of large ECG datasets.

Authors

  • Jin Liu
    School of Computer Science and Engineering, Central South University, Changsha, China.
  • Jiadong Li
    Division of Biomedical Engineering, China Medical University, China.
  • Yuxin Duan
    School of Intelligent Medicine, China Medical University, Shenyang, China.
  • Yang Zhou
    State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China.
  • Xiaoxue Fan
    Division of Biomedical Engineering, China Medical University, China.
  • Shuo Li
    Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Shijie Chang
    Department of Biomedical Engineering, China Medical University, Shenyang, 110122.

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