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ismorphism / Deepecg

Licence: mit
ECG classification programs based on ML/DL methods

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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
  1. 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 0.9 55 25 10

where 0.9 is a fraction of training size for full dataset, 55 is a first filter width, 25 is second filter width, 10 is a third filter width.

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:

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