| --- |
| license: bsd |
| language: |
| - en |
| tags: |
| - ECG |
| - Synthetic ECG |
| --- |
| |
| # deepfake-ecg |
|
|
| [Paper](https://www.nature.com/articles/s41598-021-01295-2) | [GitHub](https://github.com/vlbthambawita/deepfake-ecg) | [Pre-generated ECGs (150k)](https://osf.io/6hved/) |
| --- |
|
|
| # To generate synthetic ECGs from Hugging face |
| ```python |
| from transformers import AutoModel |
| |
| model = AutoModel.from_pretrained("deepsynthbody/deepfake_ecg", trust_remote_code=True) |
| |
| out = model(num_samples=5) |
| ``` |
|
|
|
|
|
|
| ## [Pulse2Pulse - development repo](https://github.com/vlbthambawita/Pulse2Pulse) |
| 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): |
| |
| |
|  |
| |
| ## 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: |
| ```latex |
| @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](https://choosealicense.com/licenses/mit/) |
| |
| ## For more details: |
| Please contact: vajira@simula.no, michael@simula.no |
| |
