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 divid...
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| Published in | Medical & biological engineering & computing Vol. 46; no. 4; pp. 323 - 332 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer-Verlag
01.04.2008
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0140-0118 1741-0444 |
| DOI | 10.1007/s11517-007-0280-0 |
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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 (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. |
|---|---|
| AbstractList | 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. 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.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. 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. 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. [PUBLICATION 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 sub(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 sub(2) with RBF classifiers. |
| Author | López, Miguel Álvarez, Daniel Marcos, J. Víctor Zamarrón, Carlos Hornero, Roberto del Campo, Félix |
| Author_xml | – sequence: 1 givenname: J. Víctor surname: Marcos fullname: Marcos, J. Víctor email: jvmarcos@gmail.com organization: Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid – sequence: 2 givenname: Roberto surname: Hornero fullname: Hornero, Roberto organization: Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid – sequence: 3 givenname: Daniel surname: Álvarez fullname: Álvarez, Daniel organization: Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid – sequence: 4 givenname: Félix surname: del Campo fullname: del Campo, Félix organization: Servicio de Neumología, Hospital del Río Hortega – sequence: 5 givenname: Miguel surname: López fullname: López, Miguel organization: Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid – sequence: 6 givenname: Carlos surname: Zamarrón fullname: Zamarrón, Carlos organization: Servicio de Neumología, Hospital Clínico Universitario |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/17968604$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1088_1361_6579_aaafb8 crossref_primary_10_1007_s11517_011_0777_4 crossref_primary_10_1007_s40846_017_0251_3 crossref_primary_10_1109_TBME_2011_2167971 crossref_primary_10_1007_s11517_011_0755_x crossref_primary_10_1038_s41598_019_49330_7 crossref_primary_10_1016_j_patrec_2010_07_010 crossref_primary_10_1007_s11517_009_0446_z crossref_primary_10_1142_S0129065713500202 crossref_primary_10_1007_s00405_020_06008_5 crossref_primary_10_1016_j_bspc_2018_12_015 crossref_primary_10_1016_j_cmpb_2008_05_006 crossref_primary_10_1007_s10916_008_9196_y crossref_primary_10_1007_s00500_009_0488_z crossref_primary_10_1016_j_compeleceng_2013_11_024 crossref_primary_10_1007_s11517_010_0646_6 crossref_primary_10_1080_03091900902960710 crossref_primary_10_1016_j_medengphy_2009_05_010 crossref_primary_10_3390_s23094267 crossref_primary_10_1155_2015_237878 crossref_primary_10_1007_s11517_009_0524_2 |
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| Keywords | Radial basis function neural network Obstructive sleep apnoea syndrome Data clustering Nonlinear analysis Nocturnal oximetry |
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Aging. 2002 Jan-Feb;23(1):23-6 |
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| SubjectTerms | Aged Algorithms Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Body mass index Computer Applications Diagnostics Discriminant analysis Female Hospitals Human Physiology Humans Imaging Male Medical diagnosis Methods Middle Aged Models, Statistical Neural networks Neural Networks (Computer) Original Article Oximetry Oxygen - blood Oxygen saturation Pattern Recognition, Automated Radiology ROC Curve Sensitivity and Specificity Signal processing Sleep apnea Sleep Apnea, Obstructive - diagnosis Studies |
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| Title | Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry |
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