  This paper systematically investigates the effectiveness of various
augmentations for contrastive self-supervised learning of electrocardiogram
(ECG) signals and identifies the best parameters. The baseline of our proposed
self-supervised framework consists of two main parts: the contrastive learning
and the downstream task. In the first stage, we train an encoder using a number
of augmentations to extract generalizable ECG signal representations. We then
freeze the encoder and finetune a few linear layers with different amounts of
labelled data for downstream arrhythmia detection. We then experiment with
various augmentations techniques and explore a range of parameters. Our
experiments are done on PTB-XL, a large and publicly available 12-lead ECG
dataset. The results show that applying augmentations in a specific range of
complexities works better for self-supervised contrastive learning. For
instance, when adding Gaussian noise, a sigma in the range of 0.1 to 0.2
achieves better results, while poor training occurs when the added noise is too
small or too large (outside of the specified range). A similar trend is
observed with other augmentations, demonstrating the importance of selecting
the optimum level of difficulty for the added augmentations, as augmentations
that are too simple will not result in effective training, while augmentations
that are too difficult will also prevent the model from effective learning of
generalized representations. Our work can influence future research on
self-supervised contrastive learning on bio-signals and aid in selecting
optimum parameters for different augmentations.
