Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder
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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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 0; 2025: 1.
8 papers.
- 2023Synthetic ECG Signal Generation using Probabilistic Diffusion Modelscited by 22
- 2022ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseasescited by 14
- 2023Deep Generative Models for Physiological Signals: A Systematic Literature Reviewcited by 7
- 2021Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial networkcited by 6
- 2023Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Datasetcited by 3
- 2023Joint optimization of a β-VAE for ECG task-specific feature extractioncited by 2
- 2022CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signalscited by 1
- 2025Synthetic Electrogram Generation with Variational Autoencoders for ECGIcited by 0
- 2025Synthetic Electrogram Generation with Variational Autoencoders for ECGI16 Dec 2025
- 2023Deep Generative Models for Physiological Signals: A Systematic Literature Review12 Jul 2023
- 2023Joint optimization of a β-VAE for ECG task-specific feature extraction28 Mar 2023
- 2023Synthetic ECG Signal Generation using Probabilistic Diffusion Models4 Mar 2023
- 2023Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset24 Feb 2023
- 2022CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals15 Nov 2022
- 2022ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases21 Jul 2022
- 2021Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial network2 Jun 2021