11 / 2020 – 05 / 2021
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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