Automatic Diagnosis of Obstructive Sleep Apnea/Hypopnea Events Using Respiratory Signals

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dc.authoridOter, Ali/0000-0002-9546-0602
dc.contributor.authorAydogan, Osman
dc.contributor.authorOter, Ali
dc.contributor.authorGüney, Kerim
dc.contributor.authorKiymik, M. Kemal
dc.contributor.authorTuncel, Deniz
dc.date.accessioned2025-02-24T17:18:44Z
dc.date.available2025-02-24T17:18:44Z
dc.date.issued2016
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractObstructive sleep apnea is a sleep disorder which may lead to various results. While some studies used real-time systems, there are also numerous studies which focus on diagnosing Obstructive Sleep Apnea via signals obtained by polysomnography from apnea patients who spend the night in sleep laboratory. The mean, frequency and power of signals obtained from patients are frequently used. Obstructive Sleep Apnea of 74 patients were scored in this study. A visual-scoring based algorithm and a morphological filter via Artificial Neural Networks were used in order to diagnose Obstructive Sleep Apnea. After total accuracy of scoring was calculated via both methods, it was compared with visual scoring performed by the doctor. The algorithm used in the diagnosis of obstructive sleep apnea reached an average accuracy of 88.33 %, while Artificial Neural Networks and morphological filter method reached a success of 87.28 %. Scoring success was analyzed after it was grouped based on apnea/hypopnea. It is considered that both methods enable doctors to reduce time and costs in the diagnosis of Obstructive Sleep Apnea as well as ease of use.
dc.identifier.doi10.1007/s10916-016-0624-0
dc.identifier.issn0148-5598
dc.identifier.issn1573-689X
dc.identifier.issue12
dc.identifier.pmid27761843
dc.identifier.scopus2-s2.0-84991758404
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s10916-016-0624-0
dc.identifier.urihttps://hdl.handle.net/20.500.14440/829
dc.identifier.volume40
dc.identifier.wosWOS:000388633000024
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Medical Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250201
dc.subjectObstructive sleep apnea
dc.subjectSleep disorder
dc.subjectArtificial neural network
dc.subjectMorphological filter
dc.subjectVisual scoring
dc.titleAutomatic Diagnosis of Obstructive Sleep Apnea/Hypopnea Events Using Respiratory Signals
dc.typeArticle

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