Universal representations in cardiovascular ECG assessment: A self-supervised learning approach.

Journal: International journal of medical informatics
Published Date: Dec 1, 2024

Abstract

BACKGROUND: The 12-lead electrocardiogram (ECG) is an established modality for cardiovascular assessment. While deep learning algorithms have shown promising results for analyzing ECG data, the limited availability of labeled datasets hinders broader applications. Self-supervised learning can learn meaningful representations from the unlabeled data and transfer the knowledge to downstream tasks. This study underscores the development and validation of a self-supervised learning methodology tailored to produce universal ECG representations from longitudinally collected ECG data, applicable across a spectrum of cardiovascular assessments.

Authors

  • Zhi-Yong Liu
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital Linkou Main Branch, Taoyuan, Taiwan.
  • Ching-Heng Lin
    Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.
  • Yu-Chun Hsu
    McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.
  • Jung-Sheng Chen
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Po-Cheng Chang
    Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and Chang Gung University Medical School, Taoyuan, Taiwan.
  • Ming-Shien Wen
    Division of Cardiology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan.
  • Chang-Fu Kuo
    Department of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital, Taipei, Taiwan, ROC.

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