  This paper presents a systematic investigation into the effectiveness of
Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia
detection. We begin by conducting a novel distribution analysis on three
popular ECG-based arrhythmia datasets: PTB-XL, Chapman, and Ribeiro. To the
best of our knowledge, our study is the first to quantify these distributions
in this area. We then perform a comprehensive set of experiments using
different augmentations and parameters to evaluate the effectiveness of various
SSL methods, namely SimCRL, BYOL, and SwAV, for ECG representation learning,
where we observe the best performance achieved by SwAV. Furthermore, our
analysis shows that SSL methods achieve highly competitive results to those
achieved by supervised state-of-the-art methods. To further assess the
performance of these methods on both In-Distribution (ID) and
Out-of-Distribution (OOD) ECG data, we conduct cross-dataset training and
testing experiments. Our comprehensive experiments show almost identical
results when comparing ID and OOD schemes, indicating that SSL techniques can
learn highly effective representations that generalize well across different
OOD datasets. This finding can have major implications for ECG-based arrhythmia
detection. Lastly, to further analyze our results, we perform detailed
per-disease studies on the performance of the SSL methods on the three
datasets.

Machine Learning;Artificial Intelligence;Signal Processing