# Synthesis of Realistic ECG using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1909.09150 · HTML: https://mlsanity.com/paper/1909.09150/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 26 of 26)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Diffusion-based Conditional ECG Generation with Structured State Space Models](https://mlsanity.com/paper/2301.08227/) (2023)
- [Synthetic ECG Signal Generation using Probabilistic Diffusion Models](https://mlsanity.com/paper/2303.02475/) (2023)
- [Generative adversarial networks in time series: A survey and taxonomy](https://mlsanity.com/paper/2107.11098/) (2021)
- [DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information](https://mlsanity.com/paper/2501.05932/) (2025)
- [ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases](https://mlsanity.com/paper/2207.10670/) (2022)
- [Synthetic ECG Signal Generation Using Generative Neural Networks](https://mlsanity.com/paper/2112.03268/) (2021)
- [Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey](https://mlsanity.com/paper/2209.09239/) (2022)
- [DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis](https://mlsanity.com/paper/2306.01875/) (2023)
- [ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2107.07677/) (2021)
- [A Comprehensive Survey on Generative Diffusion Models for Structured Data](https://mlsanity.com/paper/2306.04139/) (2023)
- [Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial network](https://mlsanity.com/paper/2106.03701/) (2021)
- [Arrhythmia Classification using CGAN-augmented ECG Signals](https://mlsanity.com/paper/2202.00569/) (2022)
- [A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance](https://mlsanity.com/paper/2207.07089/) (2022)
- [Leveraging Statistical Shape Priors in GAN-based ECG Synthesis](https://mlsanity.com/paper/2211.02626/) (2022)
- [HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN](https://mlsanity.com/paper/2303.06020/) (2023)
- [Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset](https://mlsanity.com/paper/2302.13855/) (2023)
- [ECG synthesis with Neural ODE and GAN models](https://mlsanity.com/paper/2111.00314/) (2021)
- [CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals](https://mlsanity.com/paper/2211.08385/) (2022)
- [Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search](https://mlsanity.com/paper/2301.10173/) (2023)
- [Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification](https://mlsanity.com/paper/2411.18456/) (2024)
- [Generating Realistic Multi-Beat ECG Signals](https://mlsanity.com/paper/2505.18189/) (2025)
- [High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training](https://mlsanity.com/paper/2510.05492/) (2025)
- [Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)](https://mlsanity.com/paper/2401.05295/) (2024)
- [MerGen: Micro-electrode recording synthesis using a generative data-driven approach](https://mlsanity.com/paper/2503.16928/) (2025)
- [DR-net-Mamba: Selective State-Space Modeling for Long-Range ECG Time-Series Denoising](https://mlsanity.com/paper/2609.35634/) (2026)

## Abstract (arXiv)

Access to medical data is highly restricted due to its sensitive nature, preventing communities from using this data for research or clinical training. Common methods of de-identification implemented to enable the sharing of data are sometimes inadequate to protect the individuals contained in the data. For our research, we investigate the ability of generative adversarial networks (GANs) to produce realistic medical time series data which can be used without concerns over privacy. The aim is to generate synthetic ECG signals representative of normal ECG waveforms. GANs have been used successfully to generate good quality synthetic time series and have been shown to prevent re-identification of individual records. In this work, a range of GAN architectures are developed to generate synthetic sine waves and synthetic ECG. Two evaluation metrics are then used to quantitatively assess how suitable the synthetic data is for real world applications such as clinical training and data analysis. Finally, we discuss the privacy concerns associated with sharing synthetic data produced by GANs and test their ability to withstand a simple membership inference attack. For the first time we both quantitatively and qualitatively demonstrate that GAN architecture can successfully generate time series signals that are not only structurally similar to the training sets but also diverse in nature across generated samples. We also report on their ability to withstand a simple membership inference attack, protecting the privacy of the training set.