Efficient Data Augmentation Policy for Electrocardiograms

ByeongTak Lee, Yong-Yeon Jo, Seon-Yu Lim, Youngjae Song, Joon-Myoung Kwon

Abstract: We present the taxonomy of data augmentation for electrocardiogram (ECG) after reviewing various ECG augmentation methods. On the basis of the taxonomy, we demonstrate the effect of augmentation methods on the ECG classification via extensive experiments. Initially, we examine the performance trend as the magnitude of distortion increases and identify the optimal distortion magnitude. Secondly, we investigate the synergistic combinations of the transformations and identify the pairs of transformations with the greatest positive effect. Finally, based on our experimental findings, we propose an efficient augmentation policy and demonstrate that it outperforms previous augmentation policies.