Determining hearing threshold from Brain Stem Evoked Potentials - Optimising a neural network to improve classification performance
Alpsan, Dogan, Towsey, Michael W., Ozdamar, Ozcan, Tsoi, Ah Chung, & Ghista, Dhanjoo N. (1994) Determining hearing threshold from Brain Stem Evoked Potentials - Optimising a neural network to improve classification performance. IEEE Engineering in Medicine and Biology, 13(4), pp. 465-471.
Brain Stem Evoked Potentials (BAEPs) are considered the most objective measure currently available with which to determine the functional integrity of the peripheral auditory nervous system. Estimating hearing threshold from BAEP signals is a time consuming and laborious task, and therefore one which recommends itself to automation. In this paper we demonstrate that neural networks trained by back-propagation are an effective method to classify BAEPs. In order to achieve maxumum generalsiation, it was necesary to fine tune the learning parameters. Although this step can be time consuming, it has important clinical consequences.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Journal Article|
|Keywords:||hearing, Brain Stem Evoked Potentials, neural networks|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 1994 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||15 May 2007|
|Last Modified:||09 Jun 2010 12:40|
Repository Staff Only: item control page