deepfake-ecg

Paper | GitHub | Pre-generated ECGs (150k)

Pulse2Pulse - development repo

If you want to train the model from scratch, please refere our development repository Pulse2Pulse.

Generate unlimited realistic deepfake ECGs using the deep generative model:Pulse2pulse introduced in our full paper here: https://doi.org/10.1038/s41598-021-01295-2 (DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine)

Installation

Use the package manager pip to install deepfake-ecg generator.

pip install deepfake-ecg

Usage

The generator functions can generate DeepFake ECGs with 8-lead values [lead names from first coloum to eighth colum: 'I','II','V1','V2','V3','V4','V5','V6'] for 10s (5000 values per lead). These 8-leads format can be converted to 12-leads format using the following equations.

lead III value = (lead II value) - (lead I value)
lead aVR value = -0.5*(lead I value + lead II value)
lead aVL value = lead I value - 0.5 * lead II value
lead aVF value = lead II value - 0.5 * lead I value

Run on CPU (default setting)

import deepfakeecg

#deepfakeecg.generate("number of ECG to generate", "Path to generate", "start file ids from this number", "device to run") 

deepfakeecg.generate(5, ".", start_id=0, run_device="cpu") # Generate 5 ECGs to the current folder starting from id=0

Run on GPU

import deepfakeecg

#deepfakeecg.generate("number of ECG to generate", "Path to generate", "start file ids from this number", "device to run") 

deepfakeecg.generate(5, ".", start_id=0, run_device="cuda") # Generate 5 ECGs to the current folder starting from id=0

Pre-generated DeepFake ECGs and corresponding MUSE reports are here: https://osf.io/6hved/

- In this repository, there are two DeepFake datasets:
    1. 150k dataset - Randomly generated 150k DeepFakeECGs
    2. Filtered all normals dataset - Only "Normal" ECGs filtered using the MUSE analysis report

A real ECG vs a DeepFake ECG (from left to right):

A sample DeepFake ECG:

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Citation:

@article{thambawita2021deepfake,
  title={DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine},
  author={Thambawita, Vajira and Isaksen, Jonas L and Hicks, Steven A and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Ellervik, Christina and Olesen, Morten Salling and Hansen, Torben and others},
  journal={Scientific reports},
  volume={11},
  number={1},
  pages={1--8},
  year={2021},
  publisher={Nature Publishing Group}
}

License

MIT

For more details:

Please contact: vajira@simula.no, michael@simula.no

vlbthambawita / deepfake-ecg