Random Forest-Based Classification of Heart Rate Variability Signals by Using Combinations of Linear and Nonlinear Features (CROSBI ID 563800)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Jović, Alan ; Bogunović, Nikola
engleski
Random Forest-Based Classification of Heart Rate Variability Signals by Using Combinations of Linear and Nonlinear Features
The goal of this paper is to assess various combinations of heart rate variability (HRV) features in successful classification of four different cardiac rhythms. The rhythms include: normal, congestive heart failure, supraventricular arrhythmia, and any arrhythmia. We approach the problem of automatic cardiac rhythm classification from HRV by employing several features’ schemes. The schemes are evaluated using the random forest classifier. We extracted a total of 78 linear and nonlinear features. Highest results were achieved for normal/supraventricular arrhythmia classification (93%). A feature scheme consisting of: time domain (SDNN, RMSSD, pNN20, pNN50, HTI), frequency domain (Total PSD, VLF, LF, HF, LF/HF), SD1/SD2 ratio, Fano factor, and Allan factor features demonstrated very high classification accuracy, comparable to the results for all extracted features. Results show that nonlinear features have only minor influence on overall classification accuracy.
heart rate variability; ECG; linear features; nonlinear features; random forest
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Podaci o prilogu
29-32.
2010.
objavljeno
Podaci o matičnoj publikaciji
IFMBE Proceedings Volume 29
Bamidis, Panagiotis D. ; Pallikarakis, Nicolas
Berlin: Springer
978-3-642-13038-0
1680-0737
Podaci o skupu
XII Mediterranean Conference on Medical and Biological Engineering and Computing MEDICON 2010
predavanje
27.05.2010-30.05.2010
Halkidika, Grčka