  Clinical 12-lead electrocardiography (ECG) is one of the most widely
encountered kinds of biosignals. Despite the increased availability of public
ECG datasets, label scarcity remains a central challenge in the field.
Self-supervised learning represents a promising way to alleviate this issue. In
this work, we put forward the first comprehensive assessment of self-supervised
representation learning from clinical 12-lead ECG data. To this end, we adapt
state-of-the-art self-supervised methods based on instance discrimination and
latent forecasting to the ECG domain. In a first step, we learn contrastive
representations and evaluate their quality based on linear evaluation
performance on a recently established, comprehensive, clinical ECG
classification task. In a second step, we analyze the impact of self-supervised
pretraining on finetuned ECG classifiers as compared to purely supervised
performance. For the best-performing method, an adaptation of contrastive
predictive coding, we find a linear evaluation performance only 0.5% below
supervised performance. For the finetuned models, we find improvements in
downstream performance of roughly 1% compared to supervised performance, label
efficiency, as well as robustness against physiological noise. This work
clearly establishes the feasibility of extracting discriminative
representations from ECG data via self-supervised learning and the numerous
advantages when finetuning such representations on downstream tasks as compared
to purely supervised training. As first comprehensive assessment of its kind in
the ECG domain carried out exclusively on publicly available datasets, we hope
to establish a first step towards reproducible progress in the rapidly evolving
field of representation learning for biosignals.
