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

Content Provider	Springer Nature Link
Author	Acır, Nurettin Erkan, Yasemin Bahtiyar, Yemen Alev
Copyright Year	2011
Abstract	Auditory 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.
Starting Page	859
Ending Page	867
Page Count	9
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	22
Issue Number	5
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-12-16
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Auditory brainstem response Support vector machine Feature selection Evoked potential estimation Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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