# Translation from Wearable PPG to 12-Lead ECG

arXiv: https://arxiv.org/abs/2509.25480 · HTML: https://mlsanity.com/paper/2509.25480/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2101.12072/) (2021)
- [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Diffusion Models for Time Series Applications: A Survey](https://mlsanity.com/paper/2305.00624/) (2023)
- [Time Series Diffusion in the Frequency Domain](https://mlsanity.com/paper/2402.05933/) (2024)
- [Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation](https://mlsanity.com/paper/2308.13568/) (2023)
- [Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery](https://mlsanity.com/paper/1807.02569/) (2018)
- [Improved Healthcare Access in Low-resource Regions: A Review of Technological Solutions](https://mlsanity.com/paper/2205.10913/) (2022)
- [A Novel Deep Learning Technique for Morphology Preserved Fetal ECG Extraction from Mother ECG using 1D-CycleGAN](https://mlsanity.com/paper/2310.03759/) (2023)

## Built on by (most cited first, 1 of 1)

- [Data Warmup: Complexity-Aware Curricula for Efficient Diffusion Training](https://mlsanity.com/paper/2604.07397/) (2026)

## Abstract (arXiv)

The 12-lead electrocardiogram (ECG) is the gold standard for cardiovascular monitoring, offering superior diagnostic granularity and specificity compared to photoplethysmography (PPG). However, existing 12-lead ECG systems rely on cumbersome multi-electrode setups, limiting sustained monitoring in ambulatory settings, while current PPG-based methods fail to reconstruct multi-lead ECG due to the absence of inter-lead constraints and insufficient modeling of spatial-temporal dependencies across leads. To bridge this gap, we introduce P2Es, an innovative demographic-aware diffusion framework designed to generate clinically valid 12-lead ECG from PPG signals via three key innovations. Specifically, in the forward process, we introduce frequency-domain blurring followed by temporal noise interference to simulate real-world signal distortions. In the reverse process, we design a temporal multi-scale generation module followed by frequency deblurring. In particular, we leverage KNN-based clustering combined with contrastive learning to assign affinity matrices for the reverse process, enabling demographic-specific ECG translation. Extensive experimental results show that P2Es outperforms baseline models in 12-lead ECG reconstruction.