Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

Journal: BMC medical informatics and decision making
PMID: 39838437

Abstract

BACKGROUND: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (MsCWT) is a feature extraction technique utilized to draw out distinctive attributes of ECG signals. In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum.

Authors

  • Ling-Chun Sun
    School of Medicine, National Defense Medical Center, Taipei, Taiwan.
  • Chia-Chiang Lee
    Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, 106, Taiwan, ROC.
  • Hung-Yen Ke
    Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center;
  • Chih-Yuan Wei
    Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan.
  • Ke-Feng Lin
    Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, 106, Taiwan, ROC.
  • Shih-Sung Lin
    Department of Computer Science and Information Engineering, Chinese Culture University, Taipei, 11114, Taiwan.
  • Hsin Hsiu
    Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan; Biomedical Engineering Research Center, National Defense Medical Center, Taipei, Taiwan. Electronic address: hhsiu@mail.ntust.edu.tw.
  • Ping-Nan Chen
    Department of Biomedical Engineering, National Defense Medical Center, Taipei, 114, Taiwan, ROC. g931310@gmail.com.

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