Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/44102
Título: Application of support vector machines and Gaussian Mixture Models for the detection of obstructive sleep apnoea based on the RR series
Autores/as: Ravelo, A. G.
Travieso, C. M. 
Lorenzo, F. D.
Navarro Mesa, Juan Luis 
Martin, S.
Alonso, J. B. 
Ferrer, M. A. 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: RR series
Sleep apnoea
Gaussian Mixture Models
Support Vector Machines
Fecha de publicación: 2006
Editor/a: 1109-2750
Publicación seriada: WSEAS Transactions on Computers 
Resumen: In this paper we present the performances of two automatic statistical methods for the classification of the obstructive sleep apnoea syndrome based on the RR series obtained from the Electrocardiogram (ECG). We study the effect of working with Support Vector Machines (SVM) and compare its performance with a reference detector based on Gaussian Mixture Models (GMM). These classifications methods require two previous stages: preprocessing and feature extraction. Firstly, we apply a preprocessing over the ECG for estimating the R instants which is previous to feature extraction. Secondly, a power-ratio-based coefficient (PRC) and a Linear Frequency Cepstral Coefficients (LFCC) parameterization over the RR signal is applied to extract the relevant characteristics. We fix the set of features for both classification methods.
URI: http://hdl.handle.net/10553/44102
ISSN: 1109-2750
Fuente: WSEAS Transactions on Computers[ISSN 1109-2750],v. 5(1), p. 121-124
Colección:Artículos
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