Reproducing results from paper : Deep Learning for ECG Classification

Enes Kuzucu

I prepared a Jupyter Notebook (in Colab) to reproduce the results only with minor edits in the code. The original Code belongs paper authors. Original ReadMe is below.

DeepECG

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.

Prerequisites:

• Python 3.5 and higher
• Keras framework with TensorFlow backend
• Numpy, Scipy, Pandas libs
• Scikit-learn framework

Instructions for running the program

1. Execute the training2017.zip and MIT-BH.zip files into folders training2017/ and MIT-BH/ respectively
2. 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 ECG_CNN.py cinc

• If you want to train your model on the MIT-BH dataset:

python ECG_CNN.py mit

3. 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

Additional info

Citation

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}
}

For feature extraction and hearbeat rate calculation:

• https://github.com/PIA-Group/BioSPPy (Biosignal Processing in Python)