Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients
Cardiac magnetic resonance image-based features were used to distinguish post-myocardial infarction patients into high and low arrhythmic risk groups. Seventeen features describing the size, location and texture of the scarred myocardium were used in different classifiers. In Experiment 1, a systematic testing of features and their combinations was done. SMOTE, wrapper based feature selection, and nested cross-validation were used in Experiment 2. Experiments 1 and 2 gave an accuracy of 94.4% (AUC=0.965) and 92.6% (AUC=0.921), respectively.
high and low arrhythmic risk local binary pattern sobel filter k-nearest neighbor classifier support vector machine classifier cardiac magnetic resonance image