Auditory brainstem response classification for threshold detection using estimated evoked potential data: comparison with ensemble averaged data

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dc.contributor.authorAcir, Nurettin
dc.contributor.authorErkan, Yasemin
dc.contributor.authorBahtiyar, Yemen Alev
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2013
dc.departmentNiğde ÖHÜ
dc.description.abstractAuditory brainstem response (ABR) has become a routine clinical tool in neurological and audiological assessment. ABR measurement process with ensemble averaging is very time-consuming and uncomfortable for subjects due to the more repetition of single trials. This condition also restricts the wide usability of ABR in clinical applications. Therefore, the reduction in repetitions has a great importance in ABR measurements. In this study, 488 ABR responses are used for creating two different data sets. The first set is created conventionally by ensemble averaging of 1,024 single trials for each ABR pattern. The second set is obtained from the first estimated 64 single trials of the same records for each ABRs. Estimation is realized by using a nonlinear adaptive filtering algorithm. In classification stage, a powerful classifier integrated with a feature selection algorithm is performed for each data set. In result, the classification performance for estimated ABR data with 64 repetitions is better than the classification performance of the ensemble averaged data with 1,024 repetitions. The proposed system is resulted in an accuracy of 96% for estimated ABRs. So, the proposed system can effectively be used for threshold detection in auditory assessment providing a high accuracy. While the obtained results contribute to the practical ABR usage in clinics, the great significance of it arises from the reduction in repetitions via estimation of ABRs.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [TUBITAK-105E084]
dc.description.sponsorshipThis study has been supported by The Scientific and Technological Research Council of Turkey (TUBITAK) with the project number of TUBITAK-105E084. We also would like to thank Dr. Ozcan Ozdamar and Neurosensory Engineering Lab. Staff at University of Miami, FL, USA, for collecting ABR database.
dc.identifier.doi10.1007/s00521-011-0776-2
dc.identifier.endpage867
dc.identifier.issn0941-0643
dc.identifier.issue5
dc.identifier.scopus2-s2.0-84875065038
dc.identifier.scopusqualityQ1
dc.identifier.startpage859
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-011-0776-2
dc.identifier.urihttps://hdl.handle.net/11480/4420
dc.identifier.volume22
dc.identifier.wosWOS:000316394000003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherSPRINGER
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAuditory brainstem response
dc.subjectSupport vector machine
dc.subjectFeature selection
dc.subjectEvoked potential estimation
dc.titleAuditory brainstem response classification for threshold detection using estimated evoked potential data: comparison with ensemble averaged data
dc.typeArticle

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