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R example classifier for the PhysioNet/Computing in Cardiology Challenge 2020
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README.md

Example prediction code for R for the PhysioNet/CinC Challenge 2019

Contents

This classifier uses two scripts:

  • run_12ECG_classifier.R makes classifications on 12-Leads ECG data. Add your prediction code to the run_12ECG_classifier function. load_12ECG_model.R 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.
  • driver.R 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.

Use

You can run this classifier by installing the packages in the requirements.txt file and running

Rscript driver.R 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.R, run_12ECG_classifier.R, and get_12ECG_features.R 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 Random Forest. They use statistical moments of heart rate that we computed from the WFDB signal file (the .mat file) and demographic data taken directly from the WFDB header file (the .hea file) as predictors.

The code uses an R code similar to Python Online and Offline ECG QRS Detector based on the Pan-Tomkins algorithm (https://github.com/c-labpl/qrs_detector). The code is a sample code for Physionet Challenge 2020 and not for any other experimental purposes. MIT License. Copyright (c) 2020. Andoni Elola (Universidad del Pais Vasco & Emory University).

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