Codebase for "P2E-WGAN: ECG Waveform Synthesis from PPG with Conditional Wasserstein Generative Adversarial Networks"

Paper link: https://dl.acm.org/doi/10.1145/3412841.3441979

Model

ECG feature-based WGAN-GP loss function:

End-to-end 1D convolutional network architectures:

Setup

To install the dependencies, you can run in your terminal:

pip install -r requirements.txt

A sampled dataset with ECG feature indices can be downloaded at [link].

Usage

The code is structured as follows:

data.py contains functions to transform and feed the data to the model;
models.py defines deep neural network architectures;
utils.py has utilities to benchmark the model and calculate the gradient penalty;
p2e_wgan_gp.py is the main entry to run the training and evaluation process (support running on multiple GPUs);
- --dataset_prefix flag sets the directory containing the .npy files
- --peaks_only flag sets the model to reconstruct precisely only the main features for data augmentation purposes

Citation

If you find this code helpful in any way, please cite our paper:

@inproceedings{vo2021p2e,
    title={P2E-WGAN: ECG waveform synthesis from PPG with conditional wasserstein generative adversarial networks},
    author={Vo, Khuong and Naeini, Emad Kasaeyan and Naderi, Amir and Jilani, Daniel and Rahmani, Amir M and Dutt, Nikil and Cao, Hung},
    booktitle={Proceedings of the 36th Annual ACM Symposium on Applied Computing},
    pages={1030--1036},
    year={2021}
}

Acknowledgments

The implementation of the WGAN-GP model is based on this repository: https://github.com/eriklindernoren/PyTorch-GAN

khuongav/P2E-WGAN-ecg-ppg-reconstruction