Update README.md

README.md

@@ -6,11 +6,82 @@ tags:
 - ECG
 - Synthetic ECG
 ---
-# To generate synthetic ECGs
+
+# 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):
+
+
+![GitHub Logo](samples/real_vs_fake_left_to_right_v2.png)
+
+## A sample DeepFake ECG:
+![GitHub Logo](samples/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: [email protected], [email protected]