Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2426
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dc.contributor.authorEfe, Enes-
dc.contributor.authorÖzşen, Seral-
dc.date.accessioned2022-05-23T20:22:42Z-
dc.date.available2022-05-23T20:22:42Z-
dc.date.issued2022-
dc.identifier.issn2296-9837-
dc.identifier.issn2296-9845-
dc.identifier.urihttps://doi.org/10.4028/p-2j5c10-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/2426-
dc.description.abstractSleep staging is the process of acquiring biological signals during sleep and marking them according to the stages of sleep. The procedure is performed by an experienced physician and takes more time. When this process is automated, the processing load will be reduced and the time required to identify disease will also be reduced. In this paper, 8 different transform methods for automatic sleep-staging based on convolutional neural networks (CNNs) were compared to classify sleep stages using single-channel electroencephalogram (EEG) signals. Five different labels were used to stage the sleep. These are Wake (W), NonREM-1 (N1), NonREM-2 (N2), NonREM-3 (N3), and REM (R). The classifications were done end-to-end without any hand-crafted features, ie without requiring any feature engineering. Time-Frequency components obtained by Short Time Fourier Transform, Discrete Wavelet Transform, Discrete Cosine Transform, Hilbert-Huang Transform, Discrete Gabor Transform, Fast Walsh-Hadamard Transform, Choi-Williams Distribution, and Wigner-Willie Distribution were classified with a supervised deep convolutional neural network to perform sleep staging. The discrete Cosine Transform-CNN method (DCT-CNN) showed the highest performance among the methods suggested in this paper with an F1 score of 89% and a value of 0.86 kappa. The findings of this study revealed that the transformation techniques utilized for the most accurate representation of input data are far superior to traditional approaches based on manual feature extraction, which acquires time, frequency, or nonlinear characteristics. The results of this article are expected to be useful to researchers in the development of low-cost, and easily portable devices.en_US
dc.language.isoenen_US
dc.publisherTrans Tech Publications Ltden_US
dc.relation.ispartofJournal Of Biomimetics Biomaterials And Biomedical Engineeringen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSleep Stagingen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectTime-Frequency Methodsen_US
dc.subjectEmpirical Mode Decompositionen_US
dc.subjectWavelet Transformen_US
dc.subjectEeg Signalsen_US
dc.subjectAgreementen_US
dc.titleComparison of Time-Frequency Analyzes for a Sleep Staging Application with CNNen_US
dc.typeArticleen_US
dc.identifier.doi10.4028/p-2j5c10-
dc.identifier.scopus2-s2.0-85140088261en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authoridefe, enes/0000-0002-6136-6140-
dc.identifier.volume55en_US
dc.identifier.startpage109en_US
dc.identifier.endpage130en_US
dc.identifier.wosWOS:000773993300010en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ4-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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