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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.

A method of using statistical analysis on site-sampled sphygmogram data sets and support vector machines classifier to diagnose coronary heart disease is proposed. The hemodynamic parameters derived from sphygmogram reflect the status of human cardiovascular system. Based on homodynamic parameters, the dimension reduction methods and a modified support vector machines classifier are applied to meliorate prognosis sensitivity and specificity. The test results on clinical coronary heart disease patients show that this method has obvious advantages over existing classifier method in the captioned application.

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