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.