  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.
