# Data Augmentation for Electrocardiogram Classification with Deep Neural Network

arXiv: https://arxiv.org/abs/2009.04398 · HTML: https://mlsanity.com/paper/2009.04398/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram](https://mlsanity.com/paper/2402.09450/) (2024)
- [Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey](https://mlsanity.com/paper/2310.10060/) (2023)
- [SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation](https://mlsanity.com/paper/2507.18323/) (2025)
- [Graph Structure Based Data Augmentation Method](https://mlsanity.com/paper/2205.14619/) (2022)

## Abstract (arXiv)

Electrocardiogram (ECG) is the most crucial monitoring modality to diagnose cardiovascular events. Precise and automatic detection of abnormal ECG patterns is beneficial to both physicians and patients. In the automatic detection of abnormal ECG patterns, deep neural networks (DNNs) have shown significant achievements. However, DNNs require large amount of labeled data, which are often expensive to obtain. On the other hand, recent research have shown by randomly combining data augmentations can improve image classification accuracy. Thus, in this work we explore data augmentation suitable for ECG data and propose ECG Augment. We show by introducing ECG Augment, we can improve classification of atrial fibrillation with single lead ECG data, without changing an architecture of DNN.