Classification of Biological Signals Based on Nonlinear Features (CROSBI ID 563351)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Jović, Alan ; Bogunović, Nikola
engleski
Classification of Biological Signals Based on Nonlinear Features
The problem of patient disorder classification and prediction from biological signals is addressed. We approach the problem from the perspective of nonlinear dynamical systems. Explored signals are ECG and EEG. We propose a combination of linear and nonlinear features for classification of four different types of heart rhythms through heart rate variability analysis. Classification accuracy is evaluated by three well-known machine learning algorithms: C4.5, support vector machines and random forest. The algorithms’ success rates are compared. The method of combining linear and nonlinear measures shows promising results in heart rate variability modeling. Random forest method has exhibited 99.6% classification accuracy.
Nonlinear Dynamics; Nonlinear Features; Biological Signals; ECG; EEG; HRV
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Podaci o prilogu
1340-1345.
2010.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of MELECON 2010, 15th IEEE Mediterranian Electromechanical Conference
Debono, Carl J. ; Kazmierkowski, Marian P. ; Micallef, Paul
Valletta:
978-1-4244-5794-6
Podaci o skupu
MELECON 2010
predavanje
25.04.2010-28.04.2010
Valletta, Malta