Cardiolux: ECG Arrhythmia Classification Using Non-Linear Features and Convolutional Neural Networks

[Paper]

1. Training set is - CPSC2018 training set, 6,877 recordings: link; MD5-hash: 7b6b1f1ab1b4c59169c639d379575a87 and can be downloaded here: https://storage.cloud.google.com/physionet-challenge-2020-12-lead-ecg-public/PhysioNetChallenge2020_Training_CPSC.tar.gz
2. Evaluation files: https://github.com/physionetchallenges/evaluation-2020

Run as:

Please to run our code please change the second model folder by CNN_1.model, since keras library only allowed us to save it in this addresss.Thank you:

• python train_model.py "../training_data" "./model"
• python driver.py "CNN_1.model/" "../test_data" "../results"

For Cross validation use crossval.py

Contents

From Windows/Linux, Compile as - inside the main code folder:

• python train_model.py "../training_data" "./model"
• python driver.py "./model" "../test_data" "../results"

Afterwards, compile scoring - inside the evaluation code folder:

• python evaluate_12ECG_score.py "../../test_data" "../../results" scores.csv

This code uses two main scripts to train the model and classify the data:

• train_model.py Train your model. Add your model code to the train_12ECG_model function. It also performs all file input and output. Do not edit this script or we will be unable to evaluate your submission.
• driver.py is the classifier which calls the output from your train_model script. It also performs all file input and output. Do not edit this script or we will be unable to evaluate your submission.

Check the code in these files for the input and output formats for the train_model and driver scripts.

To create and save your model, you should edit train_12ECG_classifier.py script. Note that you should not change the input arguments of the train_12ECG_classifier function or add output arguments. The needed models and parameters should be saved in a separated file. In the sample code, an additional script, get_12ECG_features.py, is used to extract hand-crafted features.

To run your classifier, you should edit the run_12ECG_classifier.py script, which takes a single recording as input and outputs the predicted classes and probabilities. Please, keep the formats of both outputs as they are shown in the example. You should not change the inputs and outputs of the run_12ECG_classifier function.

Use

You can run this classifier code by installing the requirements and running

python train_model.py training_data model   
python driver.py model test_data test_outputs

where training_data is a directory of training data files, model is a directory of files for the model, test_data is the directory of test data files, and test_outputs is a directory of classifier outputs. The PhysioNet/CinC 2020 webpage provides a training database with data files and a description of the contents and structure of these files.

Branch NN_1

Keras models are saved without imputer and classes variables since they are added within run_12ECG_classifier.py To run:

• python train_model.py "../training_data" "./model"
• python driver.py "NN_1.model/" "../test_data" "../results" Inside of ./model we will save the imputer, which will contain relevant information regarding the features from the trained model and that shall be used inside the testing set

Branch windowing

This branch contains the fun_extract_data which contains a windowing functionality with non-linear features

Please cite as:

@inproceedings{cajas2020ecg,
  title={ECG Arrhythmia Classification Using Non-Linear Features and Convolutional Neural Networks},
  author={Cajas, Sebasti{\'a}n and Astaiza, Pedro and Garcia-Chicangana, David Santiago and Segura, Camilo and Lop{\'e}z, Diego M},
  booktitle={2020 Computing in Cardiology},
  pages={1--4},
  year={2020},
  organization={IEEE}
}