Clone the repository
git clone git@github.com:awni/ecg.git
If you don't have virtualenv, install it with
pip install virtualenv
Make and activate a new Python 2.7 environment
virtualenv -p python2.7 ecg_env
source ecg_env/bin/activate
Install the requirements (this may take a few minutes).
For CPU only support run
./setup.sh
To install with GPU support run
env TF=gpu ./setup.sh
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. This will walk through downloading the Physionet 2017 challenge dataset and training and evaluating a model.
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.
This work is published in the following paper in Nature Medicine
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}
}