# Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery

arXiv: https://arxiv.org/abs/1910.09570 · HTML: https://mlsanity.com/paper/1910.09570/

## Key facts

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

## Built on by (most cited first, 13 of 13)

- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://mlsanity.com/paper/2512.02180/) (2025)
- [Transfer Learning in ECG Diagnosis: Is It Effective?](https://mlsanity.com/paper/2402.02021/) (2024)
- [HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis](https://mlsanity.com/paper/2411.11896/) (2024)
- [Evaluating Feature Attribution Methods for Electrocardiogram](https://mlsanity.com/paper/2211.12702/) (2022)
- [Parallel-Learning of Invariant and Tempo-variant Attributes of Single-Lead Cardiac Signals: PLITA](https://mlsanity.com/paper/2502.21162/) (2025)
- [uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm](https://mlsanity.com/paper/2506.11238/) (2025)
- [Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals](https://mlsanity.com/paper/2509.11606/) (2025)
- [Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals](https://mlsanity.com/paper/2510.17406/) (2025)
- [Task- and Metric-Specific Signal Quality Indices for Medical Time Series](https://mlsanity.com/paper/2602.12478/) (2026)
- [The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning](https://mlsanity.com/paper/2608.12695/) (2026)
- [Beat-Synchronous Tokenization for ECG Transformers](https://mlsanity.com/paper/2608.30367/) (2026)
- [ECG-Scroll: A Long-Horizon, Streaming Benchmark and Agent Environment for Interpretation of Ambulatory Electrocardiograms](https://mlsanity.com/paper/2609.33117/) (2026)

## 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.