ECG classification programs based on ML/DL methods. There are two datasets:
- training2017.zip file contains one electrode voltage measurements taken as the difference between RA and LA electrodes with no ground. It is taken from The 2017 PhysioNet/CinC Challenge.
- MIT-BH.zip file contains two electrode voltage measurements: MLII and V5.
- Python 3.5 and higher
- Keras framework with TensorFlow backend
- Numpy, Scipy, Pandas libs
- Scikit-learn framework
- Execute the training2017.zip and MIT-BH.zip files into folders training2017/ and MIT-BH/ respectively
- If you want to use 2D Convolutional Neural Network for ECG classification then run the file CNN_ECG.py with the following commands:
- If you want to train your model on the 2017 PhysioNet/CinC Challenge dataset:
python CNN_ECG.py cinc
- If you want to train your model on the MIT-BH dataset:
python CNN_ECG.py mit
- If you want to use 1D Convolutional Neural Network for ECG classification then run the file Conv1D_ECG.py with the following commands:
python Conv1D_ECG.py
If you use my repo - then, please, cite my paper. This is a BibTex citation:
@article{pyakillya_kazachenko_mikhailovsky_2017,
author = {Boris Pyakillya, Natasha Kazachenko, Nick Mikhailovsky},
title = {Deep Learning for ECG Classification},
journal = {Journal of Physics: Conference Series},
year = {2017},
volume = {913},
pages = {1-5},
DOI={10.1088/1742-6596/913/1/012004},
url = {http://iopscience.iop.org/article/10.1088/1742-6596/913/1/012004/pdf}
}
- https://github.com/PIA-Group/BioSPPy (Biosignal Processing in Python)
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