Info hash | af04abfe9a3c96b30e5dd029eb185e19a7055272 |
Last mirror activity | 21:42:58 ago |
Size | 271.27GB (271,270,264,489 bytes) |
Added | 2019-09-24 21:35:37 |
Views | 1369 |
Hits | 2559 |
ID | 4316 |
Type | multi |
Downloaded | 392 time(s) |
Uploaded by | joecohen |
Folder | icentia11k |
Num files | 22000 files [See full list] |
Mirrors | 2 complete, 1 downloading = 3 mirror(s) total [Log in to see full list] |
icentia11k (22000 files)
10999_batched_lbls.pkl.gz | 889.54kB |
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Type: Dataset
Tags: deep learning, ECG, cardiology, ecencha, ecentia, isentia
Bibtex:
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terms= {}, license= {http://creativecommons.org/licenses/by-nc-sa/4.0/}, superseded= {}, url= {} }