  We put forward a comprehensive assessment of self-supervised representation
learning from short segments of clinical 12-lead electrocardiography (ECG)
data. To this end, we explore adaptations of state-of-the-art self-supervised
learning algorithms from computer vision (SimCLR, BYOL, SwAV) and speech (CPC).
In a first step, we learn contrastive representations and evaluate their
quality based on linear evaluation performance on a downstream classification
task. For the best-performing method, CPC, we find linear evaluation
performances only 0.8% below supervised performance. In a second step, we
analyze the impact of self-supervised pretraining on finetuned ECG classifiers
as compared to purely supervised performance and find improvements in
downstream performance of more than 1%, label efficiency, as well as an
increased robustness against physiological noise. All experiments are carried
out exclusively on publicly available datasets, the to-date largest collection
used for self-supervised representation learning from ECG data, to foster
reproducible research in the field of ECG representation learning.
