An Application of Morphological Feature Extraction and Support Vector Machines in Computerized ECG Interpretation

This paper presents a novel approach that recognizing heart rhythm with the combination of adaptive Hermite decomposition and support vector machines (SVM) classification. The novelty lies in two aspects. In the first aspect, for the goal of feature extraction, the orthogonal transformation based on Hermite basis functions is proposed to characterize the morphological features of ECG data. In the other aspect, as to the multi-class electrocardiogram (ECG) classification, the one-against-all strategy is applied to a cluster of binary SVMs. Finally, in terms of numerical experiments, the major types of heart rhythms in the MIT-BIH arrhythmia database are taken into account. The results confirm its reliability and accuracy of the proposed ECG interpreter.