Show simple item record

dc.contributor.authorAksahin, Mehmet
dc.contributor.authorErdamar, Aykut
dc.contributor.authorFirat, Hikmet
dc.contributor.authorArdic, Sadik
dc.contributor.authorEroğul, Osman
dc.date.accessioned2019-05-23T05:48:44Z
dc.date.available2019-05-23T05:48:44Z
dc.date.issued2015-04
dc.identifier.citationAkşahin, 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.issn1016-2372
dc.identifier.othernumber of pages 8
dc.identifier.urihttps://doi.org/10.4015/S1016237215500118
dc.identifier.urihttp://hdl.handle.net/20.500.11851/1017
dc.description.abstractIn 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.isoengen_US
dc.publisherWorld Scientific Publ Co Pte Ltden_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectartificial neural networken_US
dc.subjectclassificationen_US
dc.subjecthrven_US
dc.subjectcpsden_US
dc.subjectobstructive sleep apneaen_US
dc.subjectppgen_US
dc.subjectecgen_US
dc.titleObstructive Sleep Apnea Classification With Artificial Neural Network Based On Two Synchronic HRV Seriesen_US
dc.typearticleen_US
dc.relation.journalBiomedical Engineering-Applications Basis Communicationsen_US
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Biomedical Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümütr_TR
dc.identifier.volume27
dc.identifier.issue2
dc.identifier.wosWOS:000365764400001
dc.identifier.scopus2-s2.0-84928490694
dc.contributor.tobbetuauthorEroğul, Osman
dc.contributor.YOKid10187
dc.identifier.doi10.4015/S1016237215500118
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record