# Synthetic ECG Signal Generation using Probabilistic Diffusion Models

arXiv: https://arxiv.org/abs/2303.02475 · HTML: https://mlsanity.com/paper/2303.02475/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 4 Mar 2023.

## Placed in

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

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Scaling Laws for Autoregressive Generative Modeling](https://mlsanity.com/paper/2010.14701/) (2020)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [Diffusion-based Conditional ECG Generation with Structured State Space Models](https://mlsanity.com/paper/2301.08227/) (2023)
- [Synthesis of Realistic ECG using Generative Adversarial Networks](https://mlsanity.com/paper/1909.09150/) (2019)
- [ECG arrhythmia classification using a 2-D convolutional neural network](https://mlsanity.com/paper/1804.06812/) (2018)
- [LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices](https://mlsanity.com/paper/1812.04818/) (2018)
- [Synthetic ECG Signal Generation Using Generative Neural Networks](https://mlsanity.com/paper/2112.03268/) (2021)
- [ECG Heartbeat Classification Using Multimodal Fusion](https://mlsanity.com/paper/2107.09869/) (2021)
- [Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder](https://mlsanity.com/paper/2002.00254/) (2020)
- [ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features](https://mlsanity.com/paper/1812.04693/) (2018)
- [Arrhythmia Classification using CGAN-augmented ECG Signals](https://mlsanity.com/paper/2202.00569/) (2022)

## Built on by (most cited first, 22 of 22)

- [GSURE-Based Diffusion Model Training with Corrupted Data](https://mlsanity.com/paper/2305.13128/) (2023)
- [Quantum-Noise-Driven Generative Diffusion Models](https://mlsanity.com/paper/2308.12013/) (2023)
- [Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation](https://mlsanity.com/paper/2308.13568/) (2023)
- [DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information](https://mlsanity.com/paper/2501.05932/) (2025)
- [Uncertainty Quantification for Machine Learning in Healthcare: A Survey](https://mlsanity.com/paper/2505.02874/) (2025)
- [DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis](https://mlsanity.com/paper/2306.01875/) (2023)
- [Deep Generative Models for Physiological Signals: A Systematic Literature Review](https://mlsanity.com/paper/2307.06162/) (2023)
- [Diff-MTS: Temporal-Augmented Conditional Diffusion-based AIGC for Industrial Time Series Towards the Large Model Era](https://mlsanity.com/paper/2407.11501/) (2024)
- [Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Models](https://mlsanity.com/paper/2505.11482/) (2025)
- [EigenScore: OOD Detection using Covariance in Diffusion Models](https://mlsanity.com/paper/2510.07206/) (2025)
- [Bayesian ECG reconstruction using denoising diffusion generative models](https://mlsanity.com/paper/2401.05388/) (2023)
- [DiffFinger: Advancing Synthetic Fingerprint Generation through Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2405.04538/) (2024)
- [CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals](https://mlsanity.com/paper/2502.17536/) (2025)
- [ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation](https://mlsanity.com/paper/2605.17580/) (2026)
- [Fully Embedded Time-Series Generative Adversarial Networks](https://mlsanity.com/paper/2308.15730/) (2023)
- [SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation](https://mlsanity.com/paper/2407.11108/) (2024)
- [How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control](https://mlsanity.com/paper/2506.05276/) (2025)
- [Instruction-based Time Series Editing](https://mlsanity.com/paper/2508.01504/) (2025)
- [TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare](https://mlsanity.com/paper/2602.07033/) (2026)
- [Flexible framework for generating synthetic electrocardiograms and photoplethysmograms](https://mlsanity.com/paper/2408.16291/) (2024)
- [Phase-OTDR Event Detection Using Image-Based Data Transformation and Deep Learning](https://mlsanity.com/paper/2512.05830/) (2025)
- [Diffusion models for eye-gaze trajectory generation using position and velocity representations](https://mlsanity.com/paper/2609.05522/) (2026)

## Abstract (arXiv)

Deep learning image processing models have had remarkable success in recent years in generating high quality images. Particularly, the Improved Denoising Diffusion Probabilistic Models (DDPM) have shown superiority in image quality to the state-of-the-art generative models, which motivated us to investigate their capability in the generation of the synthetic electrocardiogram (ECG) signals. In this work, synthetic ECG signals are generated by the Improved DDPM and by the Wasserstein GAN with Gradient Penalty (WGAN-GP) models and then compared. To this end, we devise a pipeline to utilize DDPM in its original 2D form. First, the 1D ECG time series data are embedded into the 2D space, for which we employed the Gramian Angular Summation/Difference Fields (GASF/GADF) as well as Markov Transition Fields (MTF) to generate three 2D matrices from each ECG time series, which when put together, form a 3-channel 2D datum. Then 2D DDPM is used to generate 2D 3-channel synthetic ECG images. The 1D ECG signals are created by de-embedding the 2D generated image files back into the 1D space. This work focuses on unconditional models and the generation of Normal Sinus Beat ECG signals exclusively, where the Normal Sinus Beat class from the MIT-BIH Arrhythmia dataset is used in the training phase. The quality, distribution, and the authenticity of the generated ECG signals by each model are quantitatively evaluated and compared. Our results show that in the proposed pipeline and in the particular setting of this paper, the WGAN-GP model is consistently superior to DDPM in all the considered metrics.