Enhancing atrial fibrillation detection in PPG analysis with sparse labels through contrastive learning.

Journal: Computer methods and programs in biomedicine
PMID: 40054320

Abstract

BACKGROUND: With the advancements in wearable technology, photoplethysmography (PPG) has emerged as a promising technique for detecting atrial fibrillation (AF) due to its ability to capture cardiovascular information. However, current deep learning-based methods has strict requirements on the quantity of labeled data. To overcome this limitation, we explore the performance of self-supervised contrastive learning in PPG-based AF detection.

Authors

  • Hong Wu
    Department of Liver Surgery, Liver Transplantation Division, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Qihan Hu
  • Daomiao Wang
    Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China.
  • Shiwei Zhu
    Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China.
  • Cuiwei Yang

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