search

v3551G / general-ecg-classification-with-selfonn-pretraining

0 stars 0 forks source link

readme

A lightweight SelfONN model for general ECG classification with

pretraining

This repository is accompanying our upcoming article A lightweight SelfONN model for general ECG classification with pretraining, which builds on three datasets, (1) Icentia11K, Related paper, Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery.

(2) PTB-XL dataset, Related paper, PTB-XL, a large publicly available electrocardiography dataset.

(3) ICBEB 2018, Related paper, An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection

The code will be made available upon the manuscript is accepted.

If you use our code or referenced our paper, please acknowledge our work by citing the corresponding articles.

Setup

Environment

Install the dependencies (wfdb, pytorch, torchvision, cudatoolkit, fastai, fastprogress) by creating a conda environment:

Pytorch 1.11, Python 3.8

Project overview

  1. get_datasets.sh, the script for preparing dataset.

  2. the 'code' directory contains all the experimental code files. Change to 'code' directory, then, configs/, contains configuration files for model; experiments/, contains a file named scp_experiment.py, models/, contains model definitions, utils/, contains tool functions,

  3. data/, the root directory of all data,

  4. output/, the root directory of all experimental outputs.

Get data

Download and prepare the datasets (PTB-XL and ICBEB) via the follwing bash-script:

./get_datasets.sh

This script first downloads PTB-XL and stores it in data/ptbxl/. Afterwards all training data from the ICBEB 2018 is downloaded and temporally stored in tmp_data/. After downloading and unzipping, code/utils/convert_ICBEB.py is called which stores the data in appropriate format in data/ICBEB/.

For preparing the Icentia11K dataset, since this dataset has a storage of over 250 GB, we suggest you download the dataset firstly, and place the dataset outside the project, and access this dataset through soft link. After downloading and unzipping, code/utils/convert_Icentia11k.py is called which stores the data in appropriate format in data/icentia11k/.

Reproduce results from the paper

Change to the root directory, and then call

python -m code.reproduce_my_experiments

This will perform all experiments for all models used in the paper. Depending on the executing environment, this will take up to several hours. Once finished, all trained models, predictions and results are stored in output/, where for each experiment a sub-folder is created each with data/, models/ and results/ sub-sub-folders.

Download models and results

The models can be accessed on reasonable requests, we will also consider releasing our model later.

For customized experiments

For creating customized models for experiments, you can try as follows:

  1. create your model code/models/your_model.py which implements a standard classifier interface with fit(X_train, y_train, X_val, y_val) and predict(X)
  2. create a config file code/configs/your_configs.py with name, type and parameters (if needed)
  3. add your modeltype and model import to the cases in perform-function of code/experiments/scp_experiment.py (already added for demonstration purpose!)
  4. add your model-config to models and perform your experiment as below (adjusted code of code/reproduce_results.py):

References

Please acknowledge our work by citing our journal paper

@article{QinECGClassification2023,
doi = {xx/xxx},
url = {https://doi.org/xxx},
year = {2023},
volume={xx},
number={x},
pages={xx-xx},
publisher = {xxxx},
author = {Keke Qin, Wu Huang, Tao Zhanga, Hengyuan Zhanga, and Xiangrong Cheng},
title = {A lightweight SelfONN model for general ECG classification with pretraining},
journal = {xxxx}
}

For the PTB-XL dataset, please cite

@article{Wagner:2020PTBXL,
doi = {10.1038/s41597-020-0495-6},
url = {https://doi.org/10.1038/s41597-020-0495-6},
year = {2020},
publisher = {Springer Science and Business Media {LLC}},
volume = {7},
number = {1},
pages = {154},
author = {Patrick Wagner and Nils Strodthoff and Ralf-Dieter Bousseljot and Dieter Kreiseler and Fatima I. Lunze and Wojciech Samek and Tobias Schaeffter},
title = {{PTB}-{XL},  a large publicly available electrocardiography dataset},
journal = {Scientific Data}
}

If you use the ICBEB challenge 2018 dataset please acknowledge

@article{liu2018:icbeb,
doi = {10.1166/jmihi.2018.2442},
year = {2018},
month = sep,
publisher = {American Scientific Publishers},
volume = {8},
number = {7},
pages = {1368--1373},
author = {Feifei Liu and Chengyu Liu and Lina Zhao and Xiangyu Zhang and Xiaoling Wu and Xiaoyan Xu and Yulin Liu and Caiyun Ma and Shoushui Wei and Zhiqiang He and Jianqing Li and Eddie Ng Yin Kwee},
title = {{An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection}},
journal = {Journal of Medical Imaging and Health Informatics}
}