Feature Set Extension for Heart Rate Variability Analysis by Using Non-linear, Statistical and Geometric Measures (CROSBI ID 553407)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Jović, Alan ; Bogunović, Nikola
engleski
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.
non-linear analysis; geometric features; ECG classification; classification algorithms; random forest; RIPPER
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Podaci o prilogu
35-40.
2009.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the ITI 2009, 31st International Conference on Information Technology Interfaces
Luzar-Stiffler, Vesna ; Jarec, Iva ; Bekić, Zoran
Zagreb: Sveučilišni računski centar Sveučilišta u Zagrebu (Srce)
978-953-7138-15-8
Podaci o skupu
31st International Conference on Information Technology Interfaces, ITI 2009
predavanje
22.06.2009-25.06.2009
Dubrovnik, Hrvatska