# Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset

arXiv: https://arxiv.org/abs/2302.13855 · HTML: https://mlsanity.com/paper/2302.13855/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Deformable GANs for Pose-based Human Image Generation](https://mlsanity.com/paper/1801.00055/) (2017)
- [Synthesis of Realistic ECG using Generative Adversarial Networks](https://mlsanity.com/paper/1909.09150/) (2019)
- [Learning Audio-Visual Correlations from Variational Cross-Modal Generation](https://mlsanity.com/paper/2102.03424/) (2021)
- [Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder](https://mlsanity.com/paper/2002.00254/) (2020)
- [Noise Learning Based Denoising Autoencoder](https://mlsanity.com/paper/2101.07937/) (2021)
- [Generating Image Sequence from Description with LSTM Conditional GAN](https://mlsanity.com/paper/1806.03027/) (2018)

## Built on by (most cited first, 3 of 3)

- [X-ray2CTPA: Leveraging Diffusion Models to Enhance Pulmonary Embolism Classification](https://mlsanity.com/paper/2406.16109/) (2024)
- [Inductive transfer learning from regression to classification in ECG analysis](https://mlsanity.com/paper/2508.11656/) (2025)
- [A Usable GAN-Based Tool for Synthetic ECG Generation in Cardiac Amyloidosis Research](https://mlsanity.com/paper/2601.08260/) (2026)

## Abstract (arXiv)

Due to the data shortage problem, which is one of the major problems in the field of machine learning, the accuracy level of many applications remains well below the expected. It prevents researchers from producing new artificial intelligence-based systems with the available data. This problem can be solved by generating new synthetic data with augmentation methods. In this study, new ECG signals are produced using MIT-BIH Arrhythmia Database by using Generative Adversarial Neural Networks (GAN), which is a modern data augmentation method. These generated data are used for training a machine learning system and real ECG data for testing it. The obtained results show that this way the performance of the machine learning system is increased.