Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods

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dc.contributor.authorYucelbas, Cuneyt
dc.contributor.authorYucelbas, Sule
dc.contributor.authorOzsen, Seral
dc.contributor.authorTezel, Gulay
dc.contributor.authorKuccukturk, Serkan
dc.contributor.authorYosunkaya, Sebnem
dc.date.accessioned2024-02-23T13:55:50Z
dc.date.available2024-02-23T13:55:50Z
dc.date.issued2018
dc.departmentNEÜen_US
dc.description.abstractSleep staging is a significant process to diagnose sleep disorders. Like other stages, several parameters are required for the determination of N-REM2 stage. Sleep spindles (SSs) are among them. In this study, a methodology was presented to automatically determine starting and ending positions of SSs. To accomplish this, short-time Fourier transform-artificial neural networks (STFT-ANN), empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods were used. Two considerable methods which were determination envelope and thresholding of the decomposed signals by EMD and DWT were also presented in this study. A database from the EEG signals of nine healthy subjects-which consisted of 100 epochs including 172 SSs in total-was prepared. According to the test results, the highest sensitivity rate was obtained as 100 and 99.42 % for EMD and DWT methods. However, the sensitivity rate for the STFT-ANN method was recorded as 55.93 %. The results indicated that the EMD method could be confidently used in the automatic determination of SSs. Thanks to this study, the sleep experts will be able to reliably find out the epochs with SSs and also know the places of SSs in these epochs, automatically. Another important point of the study was that the sleep staging process-tiring, time-consuming and high fallibility for the experts-could be performed in less time and at higher accuracy rates.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey [113E591]; Scientific Research Projects Coordination Unit of Selcuk Universityen_US
dc.description.sponsorshipThis study is supported by the Scientific and Technological Research Council of Turkey (Project No. 113E591) and the Scientific Research Projects Coordination Unit of Selcuk University.en_US
dc.identifier.doi10.1007/s00521-016-2445-y
dc.identifier.endpage33en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-84978038844en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage17en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-016-2445-y
dc.identifier.urihttps://hdl.handle.net/20.500.12452/10977
dc.identifier.volume29en_US
dc.identifier.wosWOS:000427799900002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_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.subjectDwten_US
dc.subjectEegen_US
dc.subjectEmden_US
dc.subjectSleep Spindleen_US
dc.subjectStften_US
dc.titleAutomatic detection of sleep spindles with the use of STFT, EMD and DWT methodsen_US
dc.typeArticleen_US

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