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dc.contributor.advisorÜlgen, Ali Yekta
dc.contributor.authorBaykara, Banu
dc.date.accessioned2020-12-23T10:41:30Z
dc.date.available2020-12-23T10:41:30Z
dc.date.submitted1995
dc.date.issued2018-08-06
dc.identifier.urihttps://acikbilim.yok.gov.tr/handle/20.500.12812/327525
dc.description.abstractIn evoked potential studies, the Auditory Brainstem Response (ABR) isrecovered from the noise background by filtering methods. Auditory EvokedPotentials are measured by far field recording techniques with non-invasive scalpelectrodes; the potential generated in the brainstem provides the most accurateinfonnation about the integrity of the Auditory System.In this thesis, EEG recordings from human brain (real data) are processedby three different filtering algorithms which are developed for the detection of ABRburried in EEG data. These methods are the Adaptive Filtering CAF) algorithm andAveraging & Adaptive (AAF) algorithm. The third method is the Adaptive LineEnhancement (ALE) algorithm and has a rather different filtering structure.The algorithms are implemented on a IBM compatible PC and comparedfor their rate of convergence. Their performance is evaluated in terms of the MeanResidual Error (MRE) , the Integrated Mean Square Error (IMSE), Distortion Index(D!) and the correlation coefficient between the filter output and the template signal.The template signal is chosen as the 1024 averaged data. It is shown that ALEconverges faster than the AF and AAF algorithms. The correlation between thereference signal and the template is an important criteria for the convergence speed.AF adapts itself slowly when compared with AAF and ALE, on the other hand, itconverges faster than the averaging algorithm.Key Words: Electroencephalogram, Evoked Potential, Signal processing,Averaging, Adaptive Filtering.en_US
dc.languageEnglish
dc.language.isoen
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightsAttribution 4.0 United Statestr_TR
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBiyomühendisliktr_TR
dc.subjectBioengineeringen_US
dc.titleDetection of auditory brainstem responses by adaptive filtering
dc.typemasterThesis
dc.date.updated2018-08-06
dc.contributor.departmentBiyomedikal Mühendisliği Anabilim Dalı
dc.identifier.yokid10052614
dc.publisher.instituteBiyo-Medikal Mühendislik Enstitüsü
dc.publisher.universityBOĞAZİÇİ ÜNİVERSİTESİ
dc.identifier.thesisid364496
dc.description.pages93
dc.publisher.disciplineDiğer


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