# Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder

arXiv: https://arxiv.org/abs/2002.00254 · HTML: https://mlsanity.com/paper/2002.00254/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Synthetic ECG Signal Generation using Probabilistic Diffusion Models](https://mlsanity.com/paper/2303.02475/) (2023)
- [ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases](https://mlsanity.com/paper/2207.10670/) (2022)
- [Deep Generative Models for Physiological Signals: A Systematic Literature Review](https://mlsanity.com/paper/2307.06162/) (2023)
- [Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial network](https://mlsanity.com/paper/2106.03701/) (2021)
- [Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset](https://mlsanity.com/paper/2302.13855/) (2023)
- [Joint optimization of a β-VAE for ECG task-specific feature extraction](https://mlsanity.com/paper/2304.06476/) (2023)
- [CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals](https://mlsanity.com/paper/2211.08385/) (2022)
- [Synthetic Electrogram Generation with Variational Autoencoders for ECGI](https://mlsanity.com/paper/2512.14537/) (2025)

## Abstract (arXiv)

We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Using this method we extracted a vector of new 25 features, which in many cases can be interpreted. The generated ECG has quite natural appearance. The low value of the Maximum Mean Discrepancy metric, 0.00383, indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Also, generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for use them in supervised learning.