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+++ b/ecg/README.md
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+##### This work and code is developed by the awesome team of Awni et al, StanfordML Group. I have just modified it to work with python3 and few other changes for my comfort of use.
+
+## Install 
+
+Clone the repository
+
+```
+git clone git@github.com:manideep2510/ECG-acquisition-classification.git
+```
+
+**Python 3.5 or higher is required to run the code. To test the code the with the pretrained models, Python 3.5 is the only one it supports**
+
+Install the requirements (this may take a few minutes).
+
+For CPU only support run
+```
+cd ecg
+./setup.sh
+```
+
+To install with GPU support run
+```
+env TF=gpu ./setup.sh
+```
+
+## Training
+
+In the repo root direcotry (`ecg`) make a new directory called `saved`.
+
+```
+mkdir saved
+```
+
+To train a model use the following command, replacing `path_to_config.json`
+with an actual config:
+
+```
+python ecg/train.py path_to_config.json
+```
+
+Note that after each epoch the model is saved in
+`ecg/saved/<experiment_id>/<timestamp>/<model_id>.hdf5`.
+
+For an actual example of how to run this code on a real dataset, you can follow
+the instructions in the cinc17 [README](examples/cinc17/README.md). This will
+walk through downloading the Physionet 2017 challenge dataset and training and
+evaluating a model.
+
+## Testing
+
+After training the model for a few epochs, you can make predictions with.
+
+```
+python ecg/predict.py <dataset>.json <model>.hdf5
+```
+
+replacing `<dataset>` with an actual path to the dataset and `<model>` with the
+path to the model.
+
+## Citation and Reference
+
+This work is published in the following paper in *Nature Medicine*
+
+[Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network](https://www.nature.com/articles/s41591-018-0268-3)
+
+If you find this codebase useful for your research please cite:
+
+```
+@article{hannun2019cardiologist,
+  title={Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network},
+  author={Hannun, Awni Y and Rajpurkar, Pranav and Haghpanahi, Masoumeh and Tison, Geoffrey H and Bourn, Codie and Turakhia, Mintu P and Ng, Andrew Y},
+  journal={Nature Medicine},
+  volume={25},
+  number={1},
+  pages={65},
+  year={2019},
+  publisher={Nature Publishing Group}
+}
+```
+
+