DiffECG: Diffusion Model-Powered Label-Efficient and Personalized Arrhythmia Diagnosis

Arrhythmia diagnosis using electrocardiogram (ECG) is critical for preventing cardiovascular risks. However, existing deep learning-based methods struggle with label scarcity and contrastive learning-based methods suffer from false-negative samples, which lead to poor model generalization. Besides, due to inter-subject variability, pre-trained models cannot achieve evenly performance across individuals. Conducting model fine-tuning for each individual is computationally expensive and does not guarantee improvement. We propose DiffECG, a diffusion-based self-supervised learning framework for label-efficient and personalized arrhythmia detection. Our method utilizes a diffusion model to extract robust ECG…

• NS
  National Science Foundation

  Awards: CHE-2202693, 2202693
• DO
  Division of Chemistry

  Awards: 2202693, CHE-2202693