Can obstructive apnea and hypopnea during sleep be differentiated by using electroencephalographic frequency bands? Statistical analysis of receiver-operator curve characteristics

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dc.authoridumut, ilhan/0000-0002-5269-1128;
dc.authorwosidumut, ilhan/A-2772-2017
dc.authorwosidUçar, Erdem/G-6929-2014
dc.contributor.authorUcar, Erdem
dc.contributor.authorSut, Necdet
dc.contributor.authorGulyasar, Tevfik
dc.contributor.authorUmut, Ilhan
dc.contributor.authorOzturk, Levent
dc.date.accessioned2024-06-12T11:14:12Z
dc.date.available2024-06-12T11:14:12Z
dc.date.issued2011
dc.departmentTrakya Üniversitesien_US
dc.description.abstractAim: To investigate whether electroencephalographic (EEG) frequency bands are applicable in distinguishing abnormal respiratory events such as obstructive apnea and hypopnea in patients with sleep apnea. Materials and methods: The polysomnographic recordings of 20 patients were examined retrospectively. EEG record segments were taken from C4-A1 and C3-A2 channels and were analyzed with software that uses digital signal processing methods, developed by the study team. Percentage values of delta, theta, alpha, and beta frequency bands were evaluated through discriminant and receiver-operator curve (ROC) analysis to distinguish between apneas and hypopneas. Results: For the G4-A1 channel, delta (%) provided the highest discriminative value (AUG = 0.563; P < 0.001); on the other hand, alpha (%) gave the lowest discriminative value (AUG = 0.519; P = 0.041). Likewise, whereas for the C3-A2 channel delta (%) gave the highest discriminative value (AUG = 0.565; P < 0.001), alpha produced the lowest discriminative value (AUG = 0.501; P = 0.943). Conclusion: As a result of discriminant analysis, the accurate classification rate of hypopneas was 44.8% and the accurate classification of obstructive apneas was 63.5%. Of the 4 frequency bands, the most significant was delta. The predictive values were not at significance level.en_US
dc.identifier.doi10.3906/sag-1007-967
dc.identifier.endpage580en_US
dc.identifier.issn1300-0144
dc.identifier.issn1303-6165
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-79960769749en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage571en_US
dc.identifier.urihttps://doi.org/10.3906/sag-1007-967
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23847
dc.identifier.volume41en_US
dc.identifier.wosWOS:000294274700002en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal Of Medical Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSleep Apneaen_US
dc.subjectDigital Signal Processingen_US
dc.subjectElectroencephalographyen_US
dc.subjectReceiver-Operator Curve Characteristicsen_US
dc.subjectEegen_US
dc.titleCan obstructive apnea and hypopnea during sleep be differentiated by using electroencephalographic frequency bands? Statistical analysis of receiver-operator curve characteristicsen_US
dc.typeArticleen_US

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