https://github.com/awni/ecg

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
https://github.com/awni/ecg

Last synced: about 2 months ago
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Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

• Host: GitHub
• URL: https://github.com/awni/ecg
• Owner: awni
• License: gpl-3.0
• Created: 2016-10-27T16:35:12.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2023-03-24T23:57:31.000Z (over 1 year ago)
• Last Synced: 2024-06-10T18:15:24.847Z (3 months ago)
• Language: Python
• Homepage: https://stanfordmlgroup.github.io/projects/ecg2/
• Size: 2.35 MB
• Stars: 653
• Watchers: 42
• Forks: 262
• Open Issues: 22
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-ai-cardiology - awni/ecg - Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network (Code / Repositories)

README

        
## Install 

Clone the repository

```

git clone [email protected]: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

```

## 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///.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 .json .hdf5

```

replacing `` with an actual path to the dataset and `` 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}

}

```