This code uses two main scripts to train the model and classify the data:
train_model.mTrain your model. Add your model code to thetrain_12ECG_modelfunction. It also performs all file input and output. Do not edit this script or we will be unable to evaluate your submission.driver.mis the classifier which calls the output from your train_model. 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.m 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.m, is used to extract hand-crafted features.
To run your classifier, you should edit the run_12ECG_classifier.m 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 run_12ECG_classifier function. The needed models and parameters can be loaded in the load_12ECG_model function.
Check the code in these files for the input and output formats for the load_12ECG_model and run_12ECG_classifier functions.
You can run this prediction code by starting MATLAB and running
train_model(training_data, model)
driver(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.
The driver.m, get_12ECG_score.m, and get_12ECG_features.m scripts must be in the root path of your repository. If they are inside a folder, then the submission will be unsuccessful.
The code uses three main toolboxes:
- HRV toolbox to compute the RR intervals. https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox.git. "An Open Source Benchmarked Toolbox for Cardiovascular Waveform and Interval Analysis", Physiological measurement 39, no. 10 (2018): 105004. DOI:10.5281/zenodo.1243111; 2018.
- ECGkit to find the ECG fiducial points: https://github.com/marianux/ecg-kit.git
Demski AJ, Llamedo Soria M. "ecg-kit a Matlab Toolbox for Cardiovascular Signal Processing".
Journal of Open Research Software. 2016;4(1):e8. DOI: http://doi.org/10.5334/jors.86 - GEH parameter extraction and origin point: https://github.com/Tereshchenkolab/Global-Electrical-Heterogeneity.git and https://github.com/Tereshchenkolab/Origin.git. Perez-Alday,et al; "Importance of the Heart Vector Origin Point Definition for an ECG analysis: The Atherosclerosis Risk in Communities (ARIC) study". Comp Biol Med, Volume 104, January 2019, pages 127-138. https://doi.org/10.1016/j.compbiomed.2018.11.013 Waks JW, et al. "Global Electric Heterogeneity Risk Score for Prediction of Sudden Cardiac Death in the General Population: The Atherosclerosis Risk in Communities (ARIC) and Cardiovascular Health (CHS) Studies". Circulation. 2016;133:2222-2234.