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Obstructive Sleep Apnea Classification With Artificial Neural Network Based On Two Synchronic HRV Series

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dc.contributor.author Aksahin, Mehmet
dc.contributor.author Erdamar, Aykut
dc.contributor.author Firat, Hikmet
dc.contributor.author Ardic, Sadik
dc.contributor.author Eroğul, Osman
dc.date.accessioned 2019-05-23T05:48:44Z
dc.date.available 2019-05-23T05:48:44Z
dc.date.issued 2015-04
dc.identifier.citation Akşahin, M., Erdamar, A., Fırat, H., Ardıç, S., & Eroğul, O. (2015). Obstructive sleep apnea classification with artificial neural network based on two synchronic hrv series. Biomedical Engineering: Applications, Basis and Communications, 27(02), 1550011. en_US
dc.identifier.issn 1016-2372
dc.identifier.other number of pages 8
dc.identifier.uri https://doi.org/10.4015/S1016237215500118
dc.identifier.uri http://hdl.handle.net/20.500.11851/1017
dc.description.abstract In the present study, "obstructive sleep apnea (OSA) patients" and "non-OSA patients" were classified into two groups using with two synchronic heart rate variability (HRV) series obtained from electrocardiography (ECG) and photoplethysmography (PPG) signals. A linear synchronization method called cross power spectrum density (CPSD), commonly used on HRV series, was performed to obtain high-quality signal features to discriminate OSA from controls. To classify simultaneous sleep ECG and PPG signals recorded from OSA and non-OSA patients, various feed forward neural network (FFNN) architectures are used and mean relative absolute error (MRAE) is applied on FFNN results to show affectivities of developed algorithm. The FFNN architectures were trained with various numbers of neurons and hidden layers. The results show that HRV synchronization is directly related to sleep respiratory signals. The CPSD of the HRV series can confirm the clinical diagnosis; both groups determined by an expert physician can be 99% truly classified as a single hidden-layer FFNN structure with 0.0623 MRAE, in which the maximum and phase values of the CPSD curve are assigned as two features. In future work, features taken from different physiological signals can be added to define a single feature that can classify apnea without error. en_US
dc.language.iso eng en_US
dc.publisher World Scientific Publ Co Pte Ltd en_US
dc.rights info:eu-repo/semantics/closedAccess
dc.subject artificial neural network en_US
dc.subject classification en_US
dc.subject hrv en_US
dc.subject cpsd en_US
dc.subject obstructive sleep apnea en_US
dc.subject ppg en_US
dc.subject ecg en_US
dc.title Obstructive Sleep Apnea Classification With Artificial Neural Network Based On Two Synchronic HRV Series en_US
dc.type article en_US
dc.relation.journal Biomedical Engineering-Applications Basis Communications en_US
dc.contributor.department TOBB ETU, Faculty of Engineering, Department of Biomedical Engineering en_US
dc.contributor.department TOBB ETÜ, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü tr_TR
dc.identifier.volume 27
dc.identifier.issue 2
dc.identifier.wos WOS:000365764400001
dc.identifier.scopus 2-s2.0-84928490694
dc.contributor.tobbetuauthor Eroğul, Osman
dc.contributor.YOKid 10187
dc.identifier.doi 10.4015/S1016237215500118
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı tr_TR


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