We provide the weights for CardioGAN, trained on the four public datasets mentioned above. The sample code can be used to convert PPG signals to ECG. link to download weights
test_cardiogan.py
To develop a realtime application using our proposed method, we utilize an Empatica E4 to collect and transfer PPG to a computer. Our model then converts 4-second segments of input PPG to synthetic ECG.
cardiogan_realtime.py
Please see a live demonstration using this link.
Please cite our paper below when using or referring to our work.
@misc{sarkar2020cardiogan,
title={CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG},
author={Pritam Sarkar and Ali Etemad},
year={2020},
eprint={2010.00104},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Some parts of our code has been borrowed from CycleGAN TF v2.
If you have any questions or would like to discuss our work, please contact me at pritam.sarkar@queensu.ca or connect with me on LinkedIN.