New Data Pre-processing on Assessing of Obstructive Sleep Apnea Syndrome: Line Based Normalization Method (LBNM)

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dc.contributor.authorAkdemir, Bayram
dc.contributor.authorGuenes, Salih
dc.contributor.authorYosunkaya, Sebnem
dc.date.accessioned2020-03-26T17:27:18Z
dc.date.available2020-03-26T17:27:18Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description4th International Conference on Intelligent Computing -- SEP 15-18, 2008 -- Shanghai, PEOPLES R CHINAen_US
dc.description.abstractSleep disorders are a very common unawareness illness among public. Obstructive Sleep Apnea Syndrome (OSAS) is characterized with decreased oxygen saturation level and repetitive upper respiratory tract obstruction episodes during full night sleep. In the present study, we have proposed a novel data normalization method called Line Based Normalization Method (LBNM) to evaluate OSAS using real data set obtained from Polysomnography device as a diagnostic tool in patients and clinically suspected of suffering OSAS. Here, we have combined the LBNM and classification methods comprising C4.5 decision tree classifier and Artificial Neural Network (ANN) to diagnose the OSAS. Firstly, each clinical feature in OSAS dataset is scaled by LBNM method in the range of [0,I]. Secondly, normalized OSAS dataset is classified using different classifier algorithms including C4.5 decision tree classifier and ANN, respectively. The proposed normalization method was compared with min-max normalization, z-score normalization, and decimal scaling methods existing in literature on the diagnosis of OSAS. LBNM has produced very promising results on the assessing of OSAS. Also, this method could be applied to other biomedical datasets.en_US
dc.description.sponsorshipcientific Research Project of Selcuk University [08701258]en_US
dc.description.sponsorshipThis study has been supported by Scientific Research Project of Selcuk University (Project number: 08701258).en_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-3-540-85929-1
dc.identifier.issn1865-0929en_US
dc.identifier.issn1865-0937en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage185en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22529
dc.identifier.volume15en_US
dc.identifier.wosWOS:000260415400025en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER-VERLAG BERLINen_US
dc.relation.ispartofADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUESen_US
dc.relation.ispartofseriesCommunications in Computer and Information Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectObstructive Sleep Apnea Syndromeen_US
dc.subjectData Scalingen_US
dc.subjectLine Based Normalization Methoden_US
dc.subjectC4.5 Decision Tree Classifieren_US
dc.subjectLevenberg Marquart Artificial Neural Networken_US
dc.titleNew Data Pre-processing on Assessing of Obstructive Sleep Apnea Syndrome: Line Based Normalization Method (LBNM)en_US
dc.typeConference Objecten_US

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