Sleep arousal detection using one dimensional local binary pattern-based convolutional neural network

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dc.authorid0000-0002-8156-016Xen_US
dc.authorid0000-0002-1432-8360en_US
dc.contributor.authorZan, Hasan
dc.contributor.authorYıldız, Abdulnasır
dc.date.accessioned2021-10-20T11:51:43Z
dc.date.available2021-10-20T11:51:43Z
dc.date.issued2021en_US
dc.departmentMAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractSleep arousal is defined as a shift from deep sleep to light sleep or complete awakening. Arousals cause sleep deprivation by fragmenting sleep, and ultimately, many health problems. Arousals can be induced by well-studied apneas and hypopneas or other sleep orders such as hypoventilation, bruxism, respiratory effort-related arousals. Thus, detection of less-studied non-apnea/hypopnea arousals is important for diagnosis and treatment of sleep disorders. Traditionally, polysomnography (PSG) test that is recording and inspecting overnight physiological signals is used for sleep studies. In this work, a novel method based on one dimensional local binary pattern (1D-LBP) and convolutional neural network (CNN) for automatic arousal detection from polysomnography recordings is proposed. 25 recordings from PhysioNet Challenge 2018 PSG dataset are used for experiments. Each signal in PSG recordings is transformed to a new signal using 1D-LBP, and then segmented using 10-s-long sliding window. The segments are fed to a CNN model formed by stacking 25 layers for classification of non-apnea/hypopnea arousal regions from non-arousal regions. Area under precision-recall curve (AUPRC) and area under receiver operating characteristic curve (AUROC) metrics are used for performance measurement. Experimental results reflect that the proposed method shows a great promise and obtains an AUPRC of 0.934 and an AUROC of 0.866.en_US
dc.identifier.citationZan, H., & Yildiz, A. (2021). Sleep Arousal Detection Using One Dimensional Local Binary Pattern-Based Convolutional Neural Network. In 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE. https://doi.org/10.1109/inista52262.2021.9548369en_US
dc.identifier.doi10.1109/inista52262.2021.9548369en_US
dc.identifier.scopus2-s2.0-85116685751en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/inista52262.2021.9548369
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85116685751&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=fdbb73300c4b80922b2d20e56f8a90e2
dc.identifier.urihttps://hdl.handle.net/20.500.12514/2891
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofIn 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectsleep arousal, one dimensional local binary pattern, 1D-LBP, deep learning, convolutional neural network, CNN.en_US
dc.titleSleep arousal detection using one dimensional local binary pattern-based convolutional neural networken_US
dc.typeConference Objecten_US

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