# 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 CNN_ECG.py cinc

```

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

```

python CNN_ECG.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)