Example Python classifier for the PhysioNet/CinC Challenge 2020

Contents

This classifier uses three scripts:

• run_12ECG_classifier.py makes the classification of the clinical 12-Leads ECG. Add your classification code to the run_12ECG_classifier function. It calls get_12ECG_features.py and 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.
• driver.py calls load_12ECG_model once and run_12ECG_classifier many times. Both functions are in run_12ECG_classifier.py file. This script also performs all file input and output. Please do not edit this script or we may be unable to evaluate your submission.

Use

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

python driver.py 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.py, run_12ECG_classifier.py, and get_12ECG_features.py 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 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 a Python Online and Offline ECG QRS Detector based on the Pan-Tomkins algorithm. It was created and used for experimental purposes in psychophysiology and psychology. You can find more information in module documentation: https://github.com/c-labpl/qrs_detector