CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals
Journal:
arXiv
Published Date:
Feb 24, 2025
Abstract
This study addresses the challenge of reconstructing unseen ECG signals from
PPG signals, a critical task for non-invasive cardiac monitoring. While
numerous public ECG-PPG datasets are available, they lack the diversity seen in
image datasets, and data collection processes often introduce noise,
complicating ECG reconstruction from PPG even with advanced machine learning
models. To tackle these challenges, we first introduce a novel synthetic
ECG-PPG data generation technique using an ODE model to enhance training
diversity. Next, we develop a novel subject-independent PPG-to-ECG
reconstruction model that integrates contrastive learning, adversarial
learning, and attention gating, achieving results comparable to or even
surpassing existing approaches for unseen ECG reconstruction. Finally, we
examine factors such as sex and age that impact reconstruction accuracy,
emphasizing the importance of considering demographic diversity during model
training and dataset augmentation.
