# Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification

arXiv: https://arxiv.org/abs/2411.18456 · HTML: https://mlsanity.com/paper/2411.18456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 66 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://mlsanity.com/paper/1706.08500/) (2017)
- [Diffusion-TS: Interpretable Diffusion for General Time Series Generation](https://mlsanity.com/paper/2403.01742/) (2024)
- [Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization](https://mlsanity.com/paper/2012.04456/) (2020)
- [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Vector Quantized Time Series Generation with a Bidirectional Prior Model](https://mlsanity.com/paper/2303.04743/) (2023)
- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [Synthesis of Realistic ECG using Generative Adversarial Networks](https://mlsanity.com/paper/1909.09150/) (2019)
- [TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation](https://mlsanity.com/paper/2206.13676/) (2022)
- [Quick and Easy Time Series Generation with Established Image-based GANs](https://mlsanity.com/paper/1902.05624/) (2019)
- [Autoregressive Models: What Are They Good For?](https://mlsanity.com/paper/1910.07737/) (2019)

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

- [Complementarity-Preserving Generative Theory for Multimodal ECG Synthesis: A Quantum-Inspired Approach](https://mlsanity.com/paper/2603.26695/) (2026)

## Abstract (arXiv)

Deep learning models need a sufficient amount of data in order to be able to find the hidden patterns in it. It is the purpose of generative modeling to learn the data distribution, thus allowing us to sample more data and augment the original dataset. In the context of physiological data, and more specifically electrocardiogram (ECG) data, given its sensitive nature and expensive data collection, we can exploit the benefits of generative models in order to enlarge existing datasets and improve downstream tasks, in our case, classification of heart rhythm. In this work, we explore the usefulness of synthetic data generated with different generative models from Deep Learning namely Diffweave, Time-Diffusion and Time-VQVAE in order to obtain better classification results for two open source multivariate ECG datasets. Moreover, we also investigate the effects of transfer learning, by fine-tuning a synthetically pre-trained model and then progressively adding increasing proportions of real data. We conclude that although the synthetic samples resemble the real ones, the classification improvement when simply augmenting the real dataset is barely noticeable on individual datasets, but when both datasets are merged the results show an increase across all metrics for the classifiers when using synthetic samples as augmented data. From the fine-tuning results the Time-VQVAE generative model has shown to be superior to the others but not powerful enough to achieve results close to a classifier trained with real data only. In addition, methods and metrics for measuring closeness between synthetic data and the real one have been explored as a side effect of the main research questions of this study.