Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques

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dc.authorid0000-0002-0636-8645en_US
dc.authorid0000-0003-2213-5881en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorUçar, Muhammed Kürşad
dc.contributor.authorBozkurt, Mehmet Recep
dc.contributor.authorBilgin, Cahit
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:49:48Z
dc.date.available2021-06-23T19:49:48Z
dc.date.issued2018
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIt is extremely significant to identify sleep stages accurately in the diagnosis of obstructive sleep apnea. In the study, it was aimed at determining sleep and wakefulness using a practical and applicable method. For this purpose , the signal of heart rate variability (HRV) has been derived from photoplethysmography (PPG). Feature extraction has been made from PPG and HRV signals. Afterward, the features, which will represent sleep and wakefulness in the best possible way, have been selected using F-score feature selection method. The selected features were classified with k-nearest neighbors classification algorithm and support vector machines. According to the results of the classification, the classification accuracy rate was found to be 73.36 %, sensivity 0.81, and specificity 0.77. Examining the performance of the classification, classifier kappa value was obtained as 0.59, area under an receiver operating characteristic value as 0.79, tenfold cross-validation as 77.35 %, and F-measurement value as 0.79. According to the results accomplished, it was concluded that PPG and HRV signals could be used for sleep staging process. It is a great advantage that PPG signal can be measured more practically compared to the other sleep staging signals used in the literature. Improving the systems, in which these signals will be used, will make diagnosis methods more practical.en_US
dc.identifier.doi10.1007/s00521-016-2365-x
dc.identifier.endpage16en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-84973115656en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-016-2365-x
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9622
dc.identifier.volume29en_US
dc.identifier.wosWOS:000427799900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectObstructive Sleep Apneaen_US
dc.subjectAutomatic Sleep Stagingen_US
dc.subjectBiomedical SignalProcessingen_US
dc.subjectBiomedical Signal Classificationen_US
dc.subjectPhotoplethysmographyen_US
dc.subjectHeart Rate Variabilityen_US
dc.subjectk-Nearest Neighbors Classification Algorithmen_US
dc.subjectSupport Vector Machinesen_US
dc.titleAutomatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniquesen_US
dc.typeArticleen_US

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