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Self-supervised pre-training for ECG representation with inspiration from transformers & computer vision

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ECG-Representation-Learning

Self-supervised pre-training for ECG representation with inspiration from recent advancements in transformers in Natural Language Processing and Computer Vision.

The combined dataset

Name # records
St Petersburg INCART 12-lead Arrhythmia Database 75
PTB Diagnostic ECG Database 549
PTB-XL, a large publicly available electrocardiography dataset 21,837
China Physiological Signal Challenge 2018 6,877
CSPC extra/unused dataset 3,453
Georgia 12-lead ECG Challenge (G12EC) Database 10,344
A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients 10,646
Test set from paper Automatic diagnosis of the 12-lead ECG using a deep neural network 827

Note that all entires apart from the last one are part of the PhysioNet - Computing in Cardiology Challenge 2021 (CinC21). We collect the dataset from the original publishing source if available since the versions from CinC21 had records removed.

To use

1< Have the datasets linked above downloaded.

2> Modify the DIR_DSET variable in file data_path.py as instructed.

A folder named as DIR_DSET should be kept at the same level as this repository, with dataset folder names specified as in config.json.

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Self-supervised pre-training for ECG representation with inspiration from transformers & computer vision

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  • Python 92.1%
  • MATLAB 7.9%