The high prevalence of cardiovascular diseases (CVDs) calls for accessible and cost-effective continuous cardiac monitoring tools. Despite Electrocardiography (ECG) being the gold standard, continuous monitoring remains a challenge, leading to the exploration of Photoplethysmography (PPG), a promising but more basic alternative available in consumer wearables. This notion has recently spurred interest in translating PPG to ECG signals. In this work, we introduce Region-Disentangled Diffusion Model (RDDM), a novel diffusion model designed to capture the complex temporal dynamics of ECG. Traditional Diffusion models like Denoising Diffusion Probabilistic Models (DDPM) face challenges in capturing such nuances due to the indiscriminate noise addition process across the entire signal. Our proposed RDDM overcomes such limitations by incorporating a novel forward process that selectively adds noise to specific regions of interest (ROI) such as QRS complex in ECG signals, and a reverse process that disentangles the denoising of ROI and non-ROI regions. Quantitative experiments demonstrate that RDDM can generate high-fidelity ECG from PPG in as few as 10 diffusion steps, making it highly effective and computationally efficient. Additionally, to rigorously validate the usefulness of the generated ECG signals, we introduce CardioBench, a comprehensive evaluation benchmark for a variety of cardiac-related tasks including heart rate and blood pressure estimation, stress classification, and the detection of atrial fibrillation and diabetes. Our thorough experiments show that RDDM achieves state-of-the-art performance on CardioBench. To the best of our knowledge, RDDM is the first diffusion model for cross-modal signal-to-signal translation in the bio-signal domain.

翻译：心血管疾病的高发病率催生了对便捷且经济高效的连续心脏监测工具的需求。尽管心电图（ECG）是黄金标准，但连续监测仍面临挑战，这促使研究人员探索光电容积描记法（PPG）——一种在消费级可穿戴设备中具有应用前景但更为基础的替代方案。这一理念近期激发了将PPG信号转换为ECG信号的研究兴趣。本文提出区域解耦扩散模型（RDDM），这是一种旨在捕获ECG复杂时序动态特性的新型扩散模型。传统扩散模型（如去噪扩散概率模型DDPM）由于对整个信号不加区分地添加噪声，难以捕捉此类细微特征。所提出的RDDM通过以下创新克服了这一局限：其前向过程选择性地针对ECG信号中的特定感兴趣区域（如QRS波群）添加噪声；反向过程则对感兴趣区域（ROI）与非感兴趣区域（非ROI）进行解耦去噪。定量实验表明，RDDM仅需10个扩散步即可从PPG生成高保真ECG信号，兼具高效性与计算经济性。此外，为严格验证生成ECG信号的有效性，我们构建了CardioBench综合评估基准，涵盖心率估计、血压估计、压力分类、房颤检测及糖尿病诊断等心脏相关任务。全面实验显示，RDDM在CardioBench上达到了最优性能。据我们所知，RDDM是生物信号领域首个用于跨模态信号-信号转换的扩散模型。