Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer
Journal:
arXiv
Published Date:
May 28, 2025
Abstract
Cardiovascular signals such as photoplethysmography (PPG),
electrocardiography (ECG), and blood pressure (BP) are inherently correlated
and complementary, together reflecting the health of cardiovascular system.
However, their joint utilization in real-time monitoring is severely limited by
diverse acquisition challenges from noisy wearable recordings to burdened
invasive procedures. Here we propose UniCardio, a multi-modal diffusion
transformer that reconstructs low-quality signals and synthesizes unrecorded
signals in a unified generative framework. Its key innovations include a
specialized model architecture to manage the signal modalities involved in
generation tasks and a continual learning paradigm to incorporate varying
modality combinations. By exploiting the complementary nature of cardiovascular
signals, UniCardio clearly outperforms recent task-specific baselines in signal
denoising, imputation, and translation. The generated signals match the
performance of ground-truth signals in detecting abnormal health conditions and
estimating vital signs, even in unseen domains, while ensuring interpretability
for human experts. These advantages position UniCardio as a promising avenue
for advancing AI-assisted healthcare.
