RT info:eu-repo/semantics/article
T1 Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry
A1 Marcos Martín, José Víctor
A1 Hornero Sánchez, Roberto
A1 Álvarez González, Daniel
A1 Campo Matias, Félix del
A1 López-Coronado Sánchez-Fortún, Miguel
A1 Zamarrón, Carlos
AB The aim of this study is to assess the ability of radial basis function (RBF) classifiers as an assistant tool for the diagnosis of the obstructive sleep apnoea syndrome (OSAS). A total of 187 subjects suspected of suffering from OSAS were available for our research. The initial population was divided into training, validation and test sets for deriving and testing our neural classifiers. We used nonlinear features from nocturnal oxygen saturation (SaO2) to perform patients’ classification. We evaluated three different RBF construction techniques based on the following algorithms: k-means (KM), fuzzy c-means (FCM) and orthogonal least squares (OLS). A diagnostic accuracy of 86.1, 84.7 and 85.5% was provided by the networks developed with KM, FCM and OLS, respectively. The three proposed networks achieved an area under the receiver operating characteristic (ROC) curve over 0.90. Our results showed that a useful non-invasive method could be applied to diagnose OSAS from nonlinear features of SaO2 with RBF classifiers.
PB Springer
SN 0140-0118
YR 2008
FD 2008
LK https://uvadoc.uva.es/handle/10324/80455
UL https://uvadoc.uva.es/handle/10324/80455
LA spa
NO Marcos, J.V., Hornero, R., Álvarez, D. et al. Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry. Med Biol Eng Comput 46, 323–332 (2008).
NO Producción Científica
DS UVaDOC
RD 17-dic-2025