CLEP-GAN: an innovative approach to subject-independent ECG reconstruction from PPG signals.

ABSTRACT:

Background

Reconstructing ECG signals from PPG measurements is a critical task for non-invasive cardiac monitoring. While several public ECG-PPG datasets exist, they lack the diversity found in image datasets, and the data collection process often introduces noise, making ECG reconstruction from PPG signals challenging even for advanced machine learning models.

Results

We propose a novel ODE-based method for generating synthetic ECG-PPG pairs to enhance training diversity. Building on this, we introduce CLEP-GAN, a subject-independent PPG-to-ECG reconstruction framework that integrates contrastive learning, adversarial learning, and attention gating. CLEP-GAN achieves performance that matches or surpasses current state-of-the-art methods, particularly in reconstructing ECG s

SUBMITTER: Li X 

PROVIDER: S-EPMC12751419 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature