Feature set extension for heart rate variability analysis by using non-linear, statistical and geometric measures

The goal of this paper is to evaluate the application of a combination of heart rate variability features on successful classification of known heart disorders. We propose an extension over our previous work, which employs 11 features, both from non-linear and linear analysis of heart rate variability. The features were extracted from electrocardiogram recordings and analyzed in Weka system for data mining using several well-known classification algorithms: C4.5 decision tree, Bayesian network, random forest, and RIPPER rules. Significance of each feature is analyzed and the algorithms' success rates are compared. The selected combination of features has a high classification potential.

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