统一生成心电、脉搏等信号，提升可穿戴设备健康监测能力
Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer
- 用统一扩散模型处理多种心血管信号生成与修复
- 在噪声数据和缺失信号场景下表现优于专用模型
- 适合医疗AI研发者与可穿戴设备开发者参考
心电图（ECG）、光电容积脉搏波（PPG）和血压（BP）信号具有内在关联性与互补性，共同反映心血管系统健康状况。然而，由于可穿戴设备采集噪声大、侵入式测量负担重，这些信号的联合应用受到严重限制。本文提出UniCardio，一种多模态扩散变压器，在统一生成框架中实现低质量信号重建与未记录信号合成。其关键创新包括针对生成任务设计的专用模型架构，以及支持不同模态组合持续学习的范式。通过利用心血管信号间的互补性，UniCardio在信号去噪、填补与转换任务上显著优于近期专用基线模型。生成信号在异常检测和生命体征估计方面表现接近真实信号，即使在未见领域也保持高精度，并具备人类专家可解释性。该方法为人工智能辅助医疗提供了新路径。
原文摘要 · Abstract (English)
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.
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