physionetchallengeshelper / matlab-classifier-2020

MATLAB example classifier for the PhysioNet/Computing in Cardiology Challenge 2020

Example MATLAB classifier for the PhysioNet/CinC Challenge 2020

Contents

This classifier uses three scripts:

• run_12ECG_classifier.m makes classifications on 12-Leads ECG data. Add your prediction code to the run_12ECG_classifier function. load_12ECG_model.m loads model weights, etc. for making classifications. To reduce your code's run time, add any code to the load_12ECG_model function that you only need to run once, such as loading weights for your model.
• get_12ECG_features.py extract the features from the clinical time-series data. This script and function are optional, but we have included it as an example. It calls all the functions inside the Tools folder
• driver.m calls load_12ECG_model once and run_12ECG_classifier many times. 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 load_12ECG_model and run_12ECG_classifier functions.

Running

You can run this classifier code by starting MATLAB and running

driver(input_directory, output_directory)

where input_directory is a directory for input data files and output_directory is a directory for output classification files. The PhysioNet/CinC 2020 webpage provides a training database with data files and a description of the contents and structure of these files.

Submission

The driver.m, get_12ECG_score.m, and get_12ECG_features.m scripts need to be in the base or root path of the Github repository. If they are inside a subfolder, then the submission will fail.

Details

“The baseline classifiers are simple logistic regression models. They use global electrical heterogeneity (GEH) computed from the WFDB signal file (the .mat file) with the [PhysioNet Cardiovascular Signal Toolbox] and demographic data taken directly from the WFDB header file (the .hea file) as predictors.

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.
• ECG-kit 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.