Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry.
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 (SaO(2)) 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 SaO(2) with RBF classifiers.
Marcos
J Víctor
JV
Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, Camino del cementerio, s/n, 47011 Valladolid, Spain. jvmarcos@gmail.com
Hornero
Roberto
R
Alvarez
Daniel
D
del Campo
Félix
F
López
Miguel
M
Zamarrón
Carlos
C
D000368:Aged; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010092:Oximetry; D010100:Oxygen / Q000097:blood; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012680:Sensitivity and Specificity; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*
D010100:Oxygen