# Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation

arXiv: https://arxiv.org/abs/2308.13568 · HTML: https://mlsanity.com/paper/2308.13568/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 16 of its 62 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation](https://mlsanity.com/paper/2212.09478/) (2022)
- [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [Diffusion-based Conditional ECG Generation with Structured State Space Models](https://mlsanity.com/paper/2301.08227/) (2023)
- [CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG](https://mlsanity.com/paper/2010.00104/) (2020)
- [A Transformer Architecture for Stress Detection from ECG](https://mlsanity.com/paper/2108.09737/) (2021)
- [Synthetic ECG Signal Generation using Probabilistic Diffusion Models](https://mlsanity.com/paper/2303.02475/) (2023)
- [PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks](https://mlsanity.com/paper/2005.01669/) (2020)
- [ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases](https://mlsanity.com/paper/2207.10670/) (2022)
- [Performer: A Novel PPG-to-ECG Reconstruction Transformer for a Digital Biomarker of Cardiovascular Disease Detection](https://mlsanity.com/paper/2204.11795/) (2022)
- [Cross-domain Joint Dictionary Learning for ECG Inference from PPG](https://mlsanity.com/paper/2101.02362/) (2021)
- [BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram](https://mlsanity.com/paper/2111.14558/) (2021)

## Built on by (most cited first, 18 of 18)

- [Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models](https://mlsanity.com/paper/2405.11566/) (2024)
- [Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer](https://mlsanity.com/paper/2505.22306/) (2025)
- [PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection](https://mlsanity.com/paper/2509.19774/) (2025)
- [Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations](https://mlsanity.com/paper/2405.20082/) (2024)
- [UniECG: Understanding and Generating ECG in One Unified Model](https://mlsanity.com/paper/2509.18588/) (2025)
- [CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals](https://mlsanity.com/paper/2502.17536/) (2025)
- [Auto-FEDUS: Autoregressive Generative Modeling of Doppler Ultrasound Signals from Fetal Electrocardiograms](https://mlsanity.com/paper/2504.13233/) (2025)
- [On Improving PPG-Based Sleep Staging: A Pilot Study](https://mlsanity.com/paper/2508.02689/) (2025)
- [PENGUIN: General Vital Sign Reconstruction from PPG with Flow Matching State Space Model](https://mlsanity.com/paper/2602.03858/) (2026)
- [Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series](https://mlsanity.com/paper/2607.04245/) (2026)
- [Socially-Informed Reconstruction for Pedestrian Trajectory Forecasting](https://mlsanity.com/paper/2412.04673/) (2024)
- [Translation from Wearable PPG to 12-Lead ECG](https://mlsanity.com/paper/2509.25480/) (2025)
- [P2E-VQ: ECG-linked representation augmentation for PPG via discrete patch retrieval](https://mlsanity.com/paper/2608.14656/) (2026)
- [Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Transformers](https://mlsanity.com/paper/2505.21767/) (2025)
- [Diffusion-based translation between unpaired spontaneous premature neonatal EEG and fetal MEG](https://mlsanity.com/paper/2507.14224/) (2025)
- [A Masked Representation Learning to Model Cardiac Functions Using Multiple Physiological Signals](https://mlsanity.com/paper/2509.08830/) (2025)
- [PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation](https://mlsanity.com/paper/2605.12541/) (2026)
- [Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction](https://mlsanity.com/paper/2609.27473/) (2026)

## Abstract (arXiv)

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.