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A Comparison of Supervised Classification Methods for Auditory Brainstem Response Determination

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

McCullagh, PJ, Wang, HY, Lightbody, G, McAllister, HG and Zheng, H (2007) A Comparison of Supervised Classification Methods for Auditory Brainstem Response Determination. In: MEDINFO 2007, Brisbane, Australia. IOS press. Vol 129 5 pp. [Conference contribution]

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URL: http://search.informit.com.au/documentSummary;dn=785062384769244;res=E-LIBRARY

Abstract

Abstract: The ABR is commonly used in the Audiology clinic to determine and quantify hearing loss. Its interpretation is subjective, dependent upon the expertise and experience of the clinical scientist. In this study we investigated the role of machine learning for pattern classification in this domain. We extracted features from the ABRs of 85 test subjects (550 waveforms) and compared four complimentary supervised classification methods: Naïve Bayes, Support Vector Machine, Multi-Layer Perceptron and KStar. The ABR dataset comprised both high level and near threshold recordings, labeled as ‘response’ or ‘no response’ by the human expert. Features were extracted from single averaged recordings to make the classification process straightforward. A best classification accuracy of 83.4% was obtained using Naïve Bayes and five relevant features extracted from time and wavelet domains. Naïve Bayes also achieved the highest specificity (86.3%). The highest sensitivity (93.1%) was obtained with Support Vector Machine-based classification models. In terms of the overall classification accuracy, four classifiers have shown the consistent, relatively high performance, indicating the relevance of selected features and the feasibility of using machine learning and statistical classification models in the analysis of ABR.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
ID Code:8830
Deposited By:Dr Paul McCullagh
Deposited On:12 Apr 2010 16:16
Last Modified:12 Apr 2010 16:16

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