Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/4395
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Özsen, Seral | - |
dc.contributor.author | Koca, Yasin | - |
dc.contributor.author | Tezel, Gülay Tezel | - |
dc.contributor.author | Solak, Fatma Zehra | - |
dc.contributor.author | Vatansev, Hulya | - |
dc.contributor.author | Kucukturk, Serkan | - |
dc.date.accessioned | 2023-08-03T19:00:19Z | - |
dc.date.available | 2023-08-03T19:00:19Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 2296-9837 | - |
dc.identifier.issn | 2296-9845 | - |
dc.identifier.uri | https://doi.org/10.4028/p-svwo5k | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/4395 | - |
dc.description.abstract | Automatic 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.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK) [119E127] | en_US |
dc.description.sponsorship | Acknowledgment This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with project number: 119E127. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Trans Tech Publications Ltd | en_US |
dc.relation.ispartof | Journal of Biomimetics Biomaterials and Biomedical Engineering | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | signal detection | en_US |
dc.subject | discrete wavelet transform | en_US |
dc.subject | Hilbert-Huang Transform | en_US |
dc.subject | Decision-Support-System | en_US |
dc.subject | Features | en_US |
dc.subject | Signals | en_US |
dc.subject | Decomposition | en_US |
dc.subject | Networks | en_US |
dc.subject | Spectrum | en_US |
dc.subject | Domain | en_US |
dc.title | Automatic Sleep Stage Classification for the Obstructive Sleep Apnea | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.4028/p-svwo5k | - |
dc.identifier.scopus | 2-s2.0-85162739972 | en_US |
dc.department | KTÜN | en_US |
dc.authorid | Kuccukturk, Serkan/0000-0001-8445-666X | - |
dc.authorwosid | Kuccukturk, Serkan/AAA-3999-2019 | - |
dc.identifier.volume | 60 | en_US |
dc.identifier.startpage | 119 | en_US |
dc.identifier.endpage | 133 | en_US |
dc.identifier.wos | WOS:001015512800010 | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 22986589400 | - |
dc.authorscopusid | 57216861791 | - |
dc.authorscopusid | 58341763900 | - |
dc.authorscopusid | 58345648900 | - |
dc.authorscopusid | 6603362805 | - |
dc.authorscopusid | 58344365600 | - |
dc.identifier.scopusquality | Q4 | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
crisitem.author.dept | 02.13. Department of Software Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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