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metadata
license: bsd
language:
  - en
tags:
  - ECG
  - Synthetic ECG

deepfake-ecg

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

To generate synthetic ECGs from Hugging face

from transformers import AutoModel

model = AutoModel.from_pretrained("deepsynthbody/deepfake_ecg", trust_remote_code=True)

out = model(num_samples=5)

Pulse2Pulse - development repo

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


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

Pre-generated DeepFake ECGs and corresponding MUSE reports are here: https://osf.io/6hved/ or (https://huggingface.co/datasets/deepsynthbody/deepfake_ecg)

- 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):

GitHub Logo

A sample DeepFake ECG:

GitHub Logo

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