# SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification

arXiv: https://arxiv.org/abs/2006.15353 · HTML: https://mlsanity.com/paper/2006.15353/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2020.

## Built on by (most cited first, 30 of 32)

- [Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling](https://mlsanity.com/paper/2102.13156/) (2021)
- [Generative Artificial Intelligence: A Systematic Review and Applications](https://mlsanity.com/paper/2405.11029/) (2024)
- [Diffusion-based Conditional ECG Generation with Structured State Space Models](https://mlsanity.com/paper/2301.08227/) (2023)
- [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)
- [Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports](https://mlsanity.com/paper/2303.09395/) (2023)
- [Spatiotemporal Data Mining: A Survey on Challenges and Open Problems](https://mlsanity.com/paper/2103.17128/) (2021)
- [Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2401.13699/) (2024)
- [Synthetic Data in Healthcare](https://mlsanity.com/paper/2304.03243/) (2023)
- [Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation](https://mlsanity.com/paper/2511.09895/) (2025)
- [Deep Generative Models for Physiological Signals: A Systematic Literature Review](https://mlsanity.com/paper/2307.06162/) (2023)
- [Personalized Heart Disease Detection via ECG Digital Twin Generation](https://mlsanity.com/paper/2404.11171/) (2024)
- [Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial network](https://mlsanity.com/paper/2106.03701/) (2021)
- [EvoGAN: An Evolutionary Computation Assisted GAN](https://mlsanity.com/paper/2110.11583/) (2021)
- [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)
- [Learning interaction rules from multi-animal trajectories via augmented behavioral models](https://mlsanity.com/paper/2107.05326/) (2021)
- [Leveraging Statistical Shape Priors in GAN-based ECG Synthesis](https://mlsanity.com/paper/2211.02626/) (2022)
- [FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generator](https://mlsanity.com/paper/2509.10491/) (2025)
- [ECG-ATK-GAN: Robustness against Adversarial Attacks on ECGs using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2110.09983/) (2021)
- [Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks](https://mlsanity.com/paper/2210.14253/) (2022)
- [Conditional Generative Models for Simulation of EMG During Naturalistic Movements](https://mlsanity.com/paper/2211.01856/) (2022)
- [ECGAN: Self-supervised generative adversarial network for electrocardiography](https://mlsanity.com/paper/2301.09496/) (2023)
- [Bayesian ECG reconstruction using denoising diffusion generative models](https://mlsanity.com/paper/2401.05388/) (2023)
- [Complementarity-Preserving Generative Theory for Multimodal ECG Synthesis: A Quantum-Inspired Approach](https://mlsanity.com/paper/2603.26695/) (2026)
- [ECG synthesis with Neural ODE and GAN models](https://mlsanity.com/paper/2111.00314/) (2021)
- [Evolving SimGANs to Improve Abnormal Electrocardiogram Classification](https://mlsanity.com/paper/2205.10116/) (2022)
- [CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals](https://mlsanity.com/paper/2211.08385/) (2022)
- [SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation](https://mlsanity.com/paper/2407.11108/) (2024)
- [Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders](https://mlsanity.com/paper/2503.13469/) (2025)
- [ODE-Constrained Generative Modeling of Cardiac Dynamics for 12-Lead ECG Synthesis](https://mlsanity.com/paper/2409.17833/) (2024)

## Abstract (arXiv)

Generating training examples for supervised tasks is a long sought after goal in AI. We study the problem of heart signal electrocardiogram (ECG) synthesis for improved heartbeat classification. ECG synthesis is challenging: the generation of training examples for such biological-physiological systems is not straightforward, due to their dynamic nature in which the various parts of the system interact in complex ways. However, an understanding of these dynamics has been developed for years in the form of mathematical process simulators. We study how to incorporate this knowledge into the generative process by leveraging a biological simulator for the task of ECG classification. Specifically, we use a system of ordinary differential equations representing heart dynamics, and incorporate this ODE system into the optimization process of a generative adversarial network to create biologically plausible ECG training examples. We perform empirical evaluation and show that heart simulation knowledge during the generation process improves ECG classification.