Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques

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dc.authorid0000-0002-0636-8645en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0003-0673-4454
dc.authorid0000-0003-2213-5881
dc.contributor.authorUçar, Muhammed Kürşad
dc.contributor.authorBozkurt, Mehmet Recep
dc.contributor.authorBilgin, Cahit
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:45:22Z
dc.date.available2021-06-23T19:45:22Z
dc.date.issued2017
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractObstructive sleep apnea is a syndrome which is characterized by the decrease in air flow or respiratory arrest depending on upper respiratory tract obstructions recurring during sleep and often observed with the decrease in the oxygen saturation. The aim of this study was to determine the connection between the respiratory arrests and the photoplethysmography (PPG) signal in obstructive sleep apnea patients. Determination of this connection is important for the suggestion of using a new signal in diagnosis of the disease. Thirty-four time-domain features were extracted from the PPG signal in the study. The relation between these features and respiratory arrests was statistically investigated. The Mann-Whitney U test was applied to reveal whether this relation was incidental or statistically significant, and 32 out of 34 features were found statistically significant. After this stage, the features of the PPG signal were classified with k-nearest neighbors classification algorithm, radial basis function neural network, probabilistic neural network, multilayer feedforward neural network (MLFFNN) and ensemble classification method. The output of the classifiers was considered as apnea and control (normal). When the classifier results were compared, the best performance was obtained with MLFFNN. Test accuracy rate is 97.07 % and kappa value is 0.93 for MLFFNN. It has been concluded with the results obtained that respiratory arrests can be recognized through the PPG signal and the PPG signal can be used for the diagnosis of OSA.en_US
dc.identifier.doi10.1007/s00521-016-2617-9
dc.identifier.endpage2945en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-84991619029en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2931en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-016-2617-9
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9141
dc.identifier.volume28en_US
dc.identifier.wosWOS:000426865100009en_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.subjectDigital Signal Processingen_US
dc.subjectPhotoplethysmographyen_US
dc.subjectBiomedical Signal Classificationen_US
dc.subjectNeural Networken_US
dc.subjectEnsemble Classification Methodsen_US
dc.subjectStatistical Signal Processingen_US
dc.subjectMann-Whitney U Testen_US
dc.titleAutomatic detection of respiratory arrests in OSA patients using PPG and machine learning techniquesen_US
dc.typeArticleen_US

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