Automatic Sleep Stage Classification for the Obstructive Sleep Apnea

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dc.contributor.authorOzsen, Seral
dc.contributor.authorKoca, Yasin
dc.contributor.authorTezel, Gulay Tezel
dc.contributor.authorSolak, Fatma Zehra
dc.contributor.authorVatansev, Hulya
dc.contributor.authorKucukturk, Serkan
dc.date.accessioned2024-02-23T14:37:38Z
dc.date.available2024-02-23T14:37:38Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractAutomatic sleep scoring systems have been much more attention in the last decades. Whereas a wide variety of studies have been used in this subject area, the accuracies are still under acceptable limits to apply these methods to real-life data. One can find many high-accuracy studies in literature using a standard database but when it comes to using real data reaching such high performance is not straightforward. In this study, five distinct datasets were prepared using 124 persons including 93 unhealthy and 31 healthy persons. These datasets consist of time-, nonlinear-, welch-, discrete wavelet transform- and Hilbert-Huang transform features. By applying k-NN, Decision Trees, ANN, SVM, and Bagged Tree classifiers to these feature sets in various manners by using feature-selection highest classification accuracy was searched. The maximum classification accuracy was detected in the case of the Bagged Tree classifier as 95.06% with the use of 14 features among a total of 136 features. This accuracy is relatively high compared with the literature for a real-data application.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [119E127]en_US
dc.description.sponsorshipAcknowledgment This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with project number: 119E127.en_US
dc.identifier.doi10.4028/p-svwo5k
dc.identifier.endpage133en_US
dc.identifier.issn2296-9837
dc.identifier.issn2296-9845
dc.identifier.scopus2-s2.0-85162739972en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage119en_US
dc.identifier.urihttps://doi.org/10.4028/p-svwo5k
dc.identifier.urihttps://hdl.handle.net/20.500.12452/16163
dc.identifier.volume60en_US
dc.identifier.wosWOS:001015512800010en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTrans Tech Publications Ltden_US
dc.relation.ispartofJournal Of Biomimetics Biomaterials And Biomedical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSignal Detectionen_US
dc.subjectDiscrete Wavelet Transformen_US
dc.subjectHilbert-Huang Transformen_US
dc.titleAutomatic Sleep Stage Classification for the Obstructive Sleep Apneaen_US
dc.typeArticleen_US

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