Versatile cardiovascular signal generation with a unified diffusion transformer

Cardiovascular signals such as photoplethysmography, electrocardiography and blood pressure are inherently correlated and complementary, together reflecting the health of the 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 multimodal 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, as well as ensuring interpretability for human experts. These advantages establish UniCardio as a practical and robust framework for advancing artificial-intelligence-assisted healthcare. UniCardio is a unified framework for versatile multimodal cardiovascular signal generation, enabling robust signal restoration and cross-modal translation to detect abnormal conditions and estimate vital signs in real-time health monitoring.