Paper Info
Title
Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry.
Abstract
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.
Year
DOI
Venue
2008
10.1007/s11517-007-0280-0
Med. Biol. Engineering and Computing
Keywords
Field
DocType
oxygen saturation,radial basis function,k means,roc curve,receiver operator characteristic
Population,Orthogonal least squares,Receiver operating characteristic,Radial basis function,Fuzzy logic,Artificial intelligence,Cluster analysis,Mathematics,Machine learning,Obstructive sleep apnoea syndrome
Journal
Volume
Issue
ISSN
46
4
0140-0118
Citations 
PageRank 
References 
16
1.69
10
Authors
6
Name
Order
Citations
PageRank
J. Víctor Marcos1757.13
Roberto Hornero260367.74
Daniel Álvarez321225.22
Félix del Campo412618.12
Miguel López5161.69
Carlos Zamarrón6658.11