--- a
+++ b/README.md
@@ -0,0 +1,46 @@
+# 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)