  We release the largest public ECG dataset of continuous raw signals for
representation learning containing 11 thousand patients and 2 billion labelled
beats. Our goal is to enable semi-supervised ECG models to be made as well as
to discover unknown subtypes of arrhythmia and anomalous ECG signal events. To
this end, we propose an unsupervised representation learning task, evaluated in
a semi-supervised fashion. We provide a set of baselines for different feature
extractors that can be built upon. Additionally, we perform qualitative
evaluations on results from PCA embeddings, where we identify some clustering
of known subtypes indicating the potential for representation learning in
arrhythmia sub-type discovery.
