Naslov
Classification of Biological Signals Based on Nonlinear Features

Autori
Jović, Alan ; Bogunović, Nikola

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of MELECON 2010, 15th IEEE Mediterranian Electromechanical Conference / Debono, Carl J. ; Kazmierkowski, Marian P. ; Micallef, Paul - Valletta, 2010, 1340-1345

ISBN
978-1-4244-5794-6

Skup
MELECON 2010

Mjesto i datum
Valletta, Malta, 25.04.2010. - 28.04.2010

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Nonlinear Dynamics; Nonlinear Features; Biological Signals; ECG; EEG; HRV

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Kliničke medicinske znanosti

POVEZANOST RADA