RandECG: Data Augmentation for Deep Neural Network Based ECG Classification

Naoki Nonaka, Jun Seita

Abstract: In the medical field, it is quite expensive to obtain labeled data that are essential to train deep neural networks (DNNs). One way to tackle this issue is to apply data augmentation, a technique to improve classification accuracy by increasing diversity of data through random but realistic transformations. Data augmentation have shown promising results in visual domain, however, transformations applied to image data cannot be directly applied to ECG data. Here we propose RandECG, a data augmentation method tailored for electrocardiogram (ECG) data classification with deep neural networks (DNNs). We explored various transformation methods and selected suitable transformations for ECG. We tested efficacy of RandECG on two different datasets, and found that the classification accuracy of atrial fibrillation can be improved up to 3.51%, without changing an architecture of DNNs.