This repository contains the Python and MATLAB evaluation code for the PhysioNet/Computing in Cardiology Challenge 2020. The evaluate_12ECG_score script evaluates the output of your algorithm using the evaluation metric that is described on the webpage for the PhysioNet/CinC Challenge 2020. While this script reports multiple evaluation metric, we use the last score (Challenge Metric) to evaluate your algorithm.
You can run the Python evaluation code by installing the NumPy Python package and running
python evaluate_12ECG_score.py labels outputs scores.csv class_scores.csv
where labels is a directory containing files with one or more labels for each 12-lead ECG recording, such as the training database on the PhysioNet webpage; outputs is a directory containing files with outputs produced by your algorithm for those recordings; scores.csv (optional) is a collection of scores for your algorithm; and class_scores.csv (optional) is a collection of per-class scores for your algorithm.
You can run the MATLAB evaluation code by installing Python and the NumPy Python package and running
evaluate_12ECG_score(labels, outputs, scores.csv, class_scores.csv)
where labels is a directory containing files with one or more labels for each 12-lead ECG recording, such as the training database on the PhysioNet webpage; outputs is a directory containing files with outputs produced by your algorithm for those recordings; scores.csv (optional) is a collection of scores for your algorithm; and class_scores.csv (optional) is a collection of per-class scores for your algorithm.
Unable to run this code with your code? Try one of the baseline classifiers on the training data. Unable to install or run Python? Try Python, Anaconda, or your package manager.