If you want to train the model from scratch, please refer to 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).
If the package is successfully published on PyPI, install it using:
pip install deepfake-ecgIf the package is not yet available on PyPI, follow these steps to install it locally:
git clone https://github.com/vlbthambawita/deepfake-ecg.git
cd deepfake-ecgpip install -e .With venv (to be stored e.g. under ~/python-environments/deepfake-ecg):
mkdir -p ~/python-environments/deepfake-ecg
python3 -m venv ~/python-environments/deepfake-ecg
. ~/python-environments/deepfake-ecg/bin/activate
pip install -e .The generator functions can generate DeepFake ECGs with 8-lead values [Lead names from the first column to the eighth column: 'I','II','V1','V2','V3','V4','V5','V6'] for 10s (5000 values per lead). These 8-lead formats can be converted to 12-lead formats 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 GPU, if availble, else on CPU:
import deepfakeecg
deepfakeecg.generate(5, ".", start_id=0) # Generate 5 ECGs to the current folder starting from id=0import deepfakeecg
deepfakeecg.generate(5, ".", start_id=0, run_device="cpu") # Generate 5 ECGs to the current folder starting from id=0import deepfakeecg
deepfakeecg.generate(5, ".", start_id=0, run_device="cuda") # Generate 5 ECGs to the current folder starting from id=0- In this repository, there are two DeepFake datasets:
- 150k dataset - Randomly generated 150k DeepFake ECGs
- Filtered all normals dataset - Only "Normal" ECGs filtered using the MUSE analysis report
A Sample ECG generator in HuggingFace
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.
@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}
}Please contact: vajira@simula.no, michael@simula.no