Feature Set Extension for Heart Rate Variability Analysis by Using Non-linear, Statistical and Geometric Measures

被引：7

作者：

Jovic, Alan ^{[1
]}

Bogunovic, Nikola ^{[1
]}

机构：

[1] Univ Zagreb, Fac Elect Engn & Comp, HR-10000 Zagreb, Croatia

来源：

PROCEEDINGS OF THE ITI 2009 31ST INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY INTERFACES | 2009年

关键词：

non-linear analysis; geometric features; ECG classification; classification algorithms; random forest; RIPPER;

D O I：

10.1109/ITI.2009.5196051

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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 fro m 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.

引用

页码：35 / 40

页数：6

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