Data Augmentation for Electrocardiogram Classification with Deep Neural Network

TL;DR

This paper introduces ECG Augment, a data augmentation technique tailored for ECG data, which enhances deep neural network performance in classifying atrial fibrillation without altering the model architecture.

Contribution

The paper proposes ECG Augment, a novel data augmentation method specifically designed for ECG signals to improve abnormal pattern classification.

Findings

ECG Augment improves atrial fibrillation classification accuracy.

The method enhances performance without changing DNN architecture.

Data augmentation benefits ECG classification tasks.

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.