Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery
Quantitative Methods
2019-10-23 v1 Computer Vision and Pattern Recognition
Signal Processing
Applications
Machine Learning
Abstract
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.
Cite
@article{arxiv.1910.09570,
title = {Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery},
author = {Shawn Tan and Guillaume Androz and Ahmad Chamseddine and Pierre Fecteau and Aaron Courville and Yoshua Bengio and Joseph Paul Cohen},
journal= {arXiv preprint arXiv:1910.09570},
year = {2019}
}
Comments
Under Review