A comparison of supervised classification methods for auditory brainstem response determination.

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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.
LanguageEnglish
Pages1289-93
JournalStudies in health technology and informatics
Volume129
Issue numberPt 2
Publication statusPublished - 2007

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Support vector machines
Learning systems
Audition
Multilayer neural networks
Pattern recognition
Classifiers

Cite this

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title = "A comparison of supervised classification methods for auditory brainstem response determination.",
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: Na{\"i}ve 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 Na{\"i}ve Bayes and five relevant features extracted from time and wavelet domains. Na{\"i}ve 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.",
author = "Paul McCullagh and Haiying Wang and Huiru Zheng and G Lightbody and G McAllister",
year = "2007",
language = "English",
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}

A comparison of supervised classification methods for auditory brainstem response determination. / McCullagh, Paul; Wang, Haiying; Zheng, Huiru; Lightbody, G; McAllister, G.

Vol. 129, No. Pt 2, 2007, p. 1289-93.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A comparison of supervised classification methods for auditory brainstem response determination.

AU - McCullagh, Paul

AU - Wang, Haiying

AU - Zheng, Huiru

AU - Lightbody, G

AU - McAllister, G

PY - 2007

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AB - 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.

M3 - Article

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