The official implementation of the paper "Reconstructing QRS Complex from PPG by Transformed Attentional Neural Networks" https://ieeexplore.ieee.org/document/9109576
Graph Abstract
Model Architecture
Download the dataset:
- https://drive.google.com/file/d/1UwuHRKkC0YPbDAFIYvFJlFmU6_3zgjcJ/view
- https://github.com/james77777778/ppg2ecg-pytorch/releases/download/dataset/dataset.zip
And follow the instruction:
mkdir data
unzip dataset.zip -d dataAfter that, you should have following data structure:
data/
├── bidmc
│ ├── bidmc_csv
│ ├── bidmc-filtered
│ ├── bidmc-filtered-test
│ └── bidmc-filtered-train
└── uqvitalsigns
├── uqvitalsignsdata
├── uqvitalsignsdata-test
└── uqvitalsignsdata-trainThe main dataset we used in this paper can be found at
The University of Queensland Vital Signs Dataset
Download the model weights and usually we put it in ./weights.
https://drive.google.com/file/d/10aYWNkgaGCz1zU6--kN3yaW6L_9BzkhQ/view?usp=sharing
(Sorry for the inconvience. The model weights are lost.)
You can check it yourself in requirements.txt
- Ubuntu 18.04
- python 3.6
- pytorch 1.2 ...
# in your environment with pip
pip install -r requirements.txtAll the training parameters are included in config files.
# run UQVIT dataset with full model
python3 train.py --flagfile config/UQVIT.cfg
# run UQVIT dataset with LSTM baseline model
python3 train.py --flagfile config/UQVIT_LSTM.cfg
# run BIDMC dataset with full model
python3 train.py --flagfile config/BIDMC.cfgPlease see EXAMPLE.md.
Simple result:
tensorboard --logdir logsIf you use this code for your research, please cite our papers.
@ARTICLE{ppg2ecg,
author={H. -Y. {Chiu} and H. -H. {Shuai} and P. C. . -P. {Chao}},
journal={IEEE Sensors Journal},
title={Reconstructing QRS Complex From PPG by Transformed Attentional Neural Networks},
year={2020},
volume={20},
number={20},
pages={12374-12383},}