# ecg

**Repository Path**: lindsaylu/ecg

## Basic Information

- **Project Name**: ecg
- **Description**: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 1
- **Created**: 2020-03-10
- **Last Updated**: 2022-12-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Install 

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
```

## 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}
}
```