
# 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):


![Real vs Fake](real_vs_fake_left_to_right_v2.png)

## A sample DeepFake ECG:
![A regenerated sample](2879.png)


## 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
{"language": ["en"], "license": "bsd", "tags": ["ECG", "Synthetic ECG"], "pipeline_tag": "unconditional-image-generation"}
transformers pytorch pulse2pulse-2 ECG Synthetic ECG unconditional-image-generation custom_code en license:bsd endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #pulse2pulse-2 #ECG #Synthetic ECG #unconditional-image-generation #custom_code #en #license-bsd #endpoints_compatible #has_space #region-us 


# deepfake-ecg

Paper

GitHub

Pre-generated ECGs (150k)

---

# To generate synthetic ECGs from Hugging face




## 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. 




### Pre-generated DeepFake ECGs and corresponding MUSE reports are here: URL or (URL
    - 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):


!Real vs Fake

## A sample DeepFake ECG:
!A regenerated sample


## 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.


:


## License
MIT

## For more details: 
Please contact: vajira@URL, michael@URL
# deepfake-ecg

Paper

GitHub

Pre-generated ECGs (150k)

--- # To generate synthetic ECGs from Hugging face ## 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. ### Pre-generated DeepFake ECGs and corresponding MUSE reports are here: URL or (URL
    - 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):


!Real vs Fake ## A sample DeepFake ECG:
!A regenerated sample ## 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.


: ## License
MIT ## For more details: 
Please contact: vajira@URL, michael@URL
TAGS
#transformers #pytorch #pulse2pulse-2 #ECG #Synthetic ECG #unconditional-image-generation #custom_code #en #license-bsd #endpoints_compatible #has_space #region-us 
 # deepfake-ecg

Paper

GitHub

Pre-generated ECGs (150k)

--- # To generate synthetic ECGs from Hugging face ## 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. ### Pre-generated DeepFake ECGs and corresponding MUSE reports are here: URL or (URL
    - 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):


!Real vs Fake ## A sample DeepFake ECG:
!A regenerated sample ## 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.


: ## License
MIT ## For more details: 
Please contact: vajira@URL, michael@URL
TAGS
#transformers #pytorch #pulse2pulse-2 #ECG #Synthetic ECG #unconditional-image-generation #custom_code #en #license-bsd #endpoints_compatible #has_space #region-us 
# deepfake-ecg

Paper

GitHub

Pre-generated ECGs (150k)

---# To generate synthetic ECGs from Hugging face## 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.### Pre-generated DeepFake ECGs and corresponding MUSE reports are here: URL or (URL
    - 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):


!Real vs Fake## A sample DeepFake ECG:
!A regenerated sample## 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.


:## License
MIT## For more details: 
Please contact: vajira@URL, michael@URL