|
--- |
|
license: bsd |
|
language: |
|
- en |
|
tags: |
|
- ECG |
|
- Synthetic ECG |
|
pipeline_tag: text2text-generation |
|
--- |
|
|
|
# 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 |
