CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals

TL;DR

This paper introduces CLEP-GAN, a novel model for reconstructing unseen ECG signals from PPG data, leveraging synthetic data generation and advanced learning techniques to improve accuracy across diverse subjects.

Contribution

The study presents a new synthetic ECG-PPG data generation method and a subject-independent reconstruction model combining contrastive, adversarial, and attention mechanisms.

Findings

Synthetic data enhances training diversity.

Model achieves high accuracy on unseen ECG signals.

Demographic factors influence reconstruction performance.

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.