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Medical & Biological Engineering & Computing

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01.04.2008 | Original Article

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

verfasst von: J. Víctor Marcos, Roberto Hornero, Daniel Álvarez, Félix del Campo, Miguel López, Carlos Zamarrón

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 4/2008

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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.

Vorheriger Artikel Detection of compressed tracheal sound patterns with large amplitude variation during sleep

Nächster Artikel A new method for determining lumbar spine motion using Bayesian belief network

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Titel: Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry
verfasst von: J. Víctor Marcos
Roberto Hornero
Daniel Álvarez
Félix del Campo
Miguel López
Carlos Zamarrón
Publikationsdatum: 01.04.2008
Verlag: Springer-Verlag
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 4/2008
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-007-0280-0

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