Deep learning models have been successfully used in electrocardiogram (ECG) classification tasks
. One potential option to improve the current benchmark is to combat model overfitting. One
cause of overfitting is a non-representative training set. The theory of stochastic approximation
indicates that a locally optimal network design requires an infinite number of training data for
convergence, which, in practice, is not possible. Thus, the network may show a poor generalization
ability if the training and validation set are highly apart distributed . Data augmentation is a
solution to increase the diversity of training samples such that the distance between the training
and validation set is minimized .
Image data augmentations have been extensively studied in the computer vision community
in order to improve model performance. Many of them, like random cropping and flipping, proved
as effective and are commonly used as standard operations for model training. However, for time-
series signals such as ECG curves, the effects of data augmentations are less investigated. Most of
the existing validations are based on introducing randomness of digital signals, like oversampling
by window slicing  or adding some random noises at different frequencies .
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