Example prediction code for R for the PhysioNet/CinC Challenge 2019
This classifier uses two scripts:
run_12ECG_classifier.Rmakes classifications on 12-Leads ECG data. Add your prediction code to the
load_12ECG_model.Rloads model weights, etc. for making classifications. To reduce your code's run time, add any code to the
load_12ECG_modelfunction that you only need to run once, such as loading weights for your model.
run_12ECG_classifiermany 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
You can run this classifier by installing the packages in the
requirements.txt file and running
Rscript driver.R input_directory output_directory
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.
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.
“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).