Weighted SVMs and Feature Relevance Assessment in Supervised Heart Beat Classification

The diagnosis of cardiac dysfunctions requires the analysis of long-term ECG signal recordings, often containing hundreds to thousands of heart beats. In this work, automatic inter-patient classification of heart beats following AAMI guidelines is investigated. The prior of the normal class is by far larger than the other classes, and the classifier obtained by a standard SVM training is likely to act as the trivial acceptor. To avoid this inconvenience, a SVM classifier optimizing a convex approximation of the balanced classification rate rather than the standard accuracy is used. First, the assessment of feature sets previously proposed in the litterature is investigated. Second, the performances of this SVM model is compared to those of previously reported inter-patient classification models. The results show that the choice of the features is of major importance, and that some previously reported feature sets do not serve the classification performances. Also, the weighted SVM model with the best feature set selection achieves results better than previously reported inter-patient models with features extracted only from the R spike annotations.