Açık Akademik Arşiv Sistemi

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

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dc.contributor.authors Ucar, MK; Bozkurt, MR; Bilgin, C; Polat, K;
dc.date.accessioned 2020-02-27T07:01:03Z
dc.date.available 2020-02-27T07:01:03Z
dc.date.issued 2018
dc.identifier.citation Ucar, MK; Bozkurt, MR; Bilgin, C; Polat, K; (2018). Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques. NEURAL COMPUTING & APPLICATIONS, 29, 16-1
dc.identifier.issn 0941-0643
dc.identifier.uri https://doi.org/10.1007/s00521-016-2365-x
dc.identifier.uri https://hdl.handle.net/20.500.12619/64881
dc.description.abstract It 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.
dc.language English
dc.publisher SPRINGER
dc.subject Computer Science
dc.title Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques
dc.type Article
dc.identifier.volume 29
dc.identifier.startpage 1
dc.identifier.endpage 16
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/Elektrik-Elektronik Mühendisliği Bölümü
dc.contributor.saüauthor Bozkurt, Mehmet Recep
dc.contributor.saüauthor Bilgin, Cahit
dc.relation.journal NEURAL COMPUTING & APPLICATIONS
dc.identifier.wos WOS:000427799900001
dc.identifier.doi 10.1007/s00521-016-2365-x
dc.identifier.eissn 1433-3058
dc.contributor.author Bozkurt, Mehmet Recep
dc.contributor.author Bilgin, Cahit
dc.contributor.author Kemal Polat


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