# Data Augmentation for Electrocardiogram Classification with Deep Neural Network

Source: [Data Augmentation for Electrocardiogram Classification with Deep Neural Network](<https://www.sota2.com/research/paper/68fa7f781e209131cea9a3f1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T06:49:43.942Z

Authors: Naoki Nonaka, Jun Seita

Published: 2020-09-05T00:27:14.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2009.04398>)

[PDF](<https://arxiv.org/pdf/2009.04398.pdf>)