Interpretable XGBoost Based Classification of 12-lead ECGs Applying Information Theory Measures from Neuroscience

Abstract

Automated ECG classification is a standard feature in many commercial 12-Lead ECG machines. As part of the Physionet/CinC Challenge 2020, our team, 'Mad-hardmax', developed an XGBoost based classification method for the analysis of 12-Lead ECGs acquired from four different countries. Our aim is to develop an interpretable classifier that outputs diagnoses which can be traced to specific ECG features, while also testing the potential of information theoretic features for ECG diagnosis. These measures capture high-level interdependencies across ECG leads which are effective for discriminating conditions with multiple complex morphologies. On unseen test data, our algorithm achieved a challenge score of 0.155 relative to a winning score of 0.533, putting our submission in 24th position from 41 successful entries.