NDLI: Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry

Content Provider	Springer Nature Link
Author	Marcos, J. Víctor Hornero, Roberto Álvarez, Daniel del Campo, Félix López, Miguel Zamarrón, Carlos
Copyright Year	2007
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 (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.
Starting Page	323
Ending Page	332
Page Count	10
File Format	PDF
ISSN	01400118
Journal	Medical and Biological Engineering and Computing
Volume Number	46
Issue Number	4
e-ISSN	17410444
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-10-30
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Obstructive sleep apnoea syndrome Nocturnal oximetry Radial basis function neural network Data clustering Nonlinear analysis Neurosciences Computer Applications Imaging Radiology Human Physiology Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering Computer Science Applications

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