  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.
