mlsanityData to 14 Oct 2026

Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery

arXiv 1910.09570 ↗First posted 21 Oct 2019q-bio.QM cs.CV eess.SPcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2019.

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Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 5; 2026: 4.

13 papers.

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