Icentia11k: An Unsupervised ECG Representation Learning Dataset for Arrhythmia Subtype Discovery
Shawn Tan and Guillaume Androz and Ahmad Chamseddine and Pierre Fecteau and Aaron Courville and Yoshua Bengio and Joseph Paul Cohen

icentia11k (22000 files)
10999_batched_lbls.pkl.gz 889.54kB
10999_batched.pkl.gz 24.05MB
10998_batched_lbls.pkl.gz 759.26kB
10998_batched.pkl.gz 18.93MB
10997_batched_lbls.pkl.gz 900.90kB
10997_batched.pkl.gz 21.77MB
10996_batched_lbls.pkl.gz 710.42kB
10996_batched.pkl.gz 23.57MB
10995_batched_lbls.pkl.gz 758.58kB
10995_batched.pkl.gz 22.86MB
10994_batched_lbls.pkl.gz 1.74MB
10994_batched.pkl.gz 24.54MB
10993_batched_lbls.pkl.gz 967.55kB
10993_batched.pkl.gz 22.69MB
10992_batched_lbls.pkl.gz 844.82kB
10992_batched.pkl.gz 19.45MB
10991_batched_lbls.pkl.gz 1.03MB
10991_batched.pkl.gz 43.23MB
10990_batched_lbls.pkl.gz 2.18MB
10990_batched.pkl.gz 51.35MB
10989_batched_lbls.pkl.gz 691.36kB
10989_batched.pkl.gz 16.38MB
10988_batched_lbls.pkl.gz 987.64kB
10988_batched.pkl.gz 26.76MB
10987_batched_lbls.pkl.gz 847.66kB
10987_batched.pkl.gz 24.15MB
10986_batched_lbls.pkl.gz 835.89kB
10986_batched.pkl.gz 21.14MB
10985_batched_lbls.pkl.gz 674.90kB
10985_batched.pkl.gz 20.12MB
10984_batched_lbls.pkl.gz 616.36kB
10984_batched.pkl.gz 20.44MB
10983_batched_lbls.pkl.gz 1.12MB
10983_batched.pkl.gz 24.04MB
10982_batched_lbls.pkl.gz 817.35kB
10982_batched.pkl.gz 20.88MB
10981_batched_lbls.pkl.gz 837.91kB
10981_batched.pkl.gz 38.83MB
10980_batched_lbls.pkl.gz 1.19MB
10980_batched.pkl.gz 32.36MB
10979_batched_lbls.pkl.gz 1.76MB
10979_batched.pkl.gz 25.73MB
10978_batched_lbls.pkl.gz 687.49kB
10978_batched.pkl.gz 25.37MB
10977_batched_lbls.pkl.gz 790.54kB
10977_batched.pkl.gz 18.21MB
10976_batched_lbls.pkl.gz 835.86kB
10976_batched.pkl.gz 36.05MB
10975_batched_lbls.pkl.gz 929.27kB
Too many files! Click here to view them all.
Type: Dataset
Tags: deep learning, ECG, cardiology, ecencha, ecentia, isentia

Bibtex:
@article{,
title= {Icentia11k: An Unsupervised ECG Representation Learning Dataset for Arrhythmia Subtype Discovery},
keywords= {deep learning, ECG, cardiology, ecencha, ecentia, isentia},
author= {Shawn Tan and Guillaume Androz and Ahmad Chamseddine and Pierre Fecteau and Aaron Courville and Yoshua Bengio and Joseph Paul Cohen},
abstract= {We release the largest public ECG dataset of raw signals for representation learning containing over 11k 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.

https://i.imgur.com/5PxNneL.png

License:
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) 
http://creativecommons.org/licenses/by-nc-sa/4.0/},
terms= {},
license= {http://creativecommons.org/licenses/by-nc-sa/4.0/},
superseded= {},
url= {}
}

Hosted by users:

Send Feedback