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Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery
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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.
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Cited by 2 Pith papers
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The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning
On Icentia11k, self-supervised ECG models trained on 5-10 minute windows with continuous CNN patch embeddings outperform 16-second and vector-quantized models on AFib/AFL detection and patient retrieval.
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FADE: Forecasting for Anomaly Detection on ECG
A self-supervised ECG forecasting model, trained only on normal signals, detects heartbeat and arrhythmia anomalies by measuring the error between its forecast and the real signal, achieving 83.84% anomaly accuracy an...
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