Welcome to the InfoSci Platform
The Use of Artificial Neural Networks for Objective Determination of Hearing Threshold Using the Auditory Brainstem Response

The Use of Artificial Neural Networks for Objective Determination of Hearing Threshold Using the Auditory Brainstem Response

Robert T. Davey, Paul J. McCullagh, H. Gerry McAllister, H. Glen Houston
Copyright: © 2006 |Pages: 22
ISBN13: 9781591408482|ISBN10: 1591408482|ISBN13 Softcover: 9781591408499|EISBN13: 9781591408505
DOI: 10.4018/978-1-59140-848-2.ch009
Cite Chapter Cite Chapter

MLA

Davey, Robert T., et al. "The Use of Artificial Neural Networks for Objective Determination of Hearing Threshold Using the Auditory Brainstem Response." Neural Networks in Healthcare: Potential and Challenges, edited by Rezaul Begg, et al., IGI Global, 2006, pp. 195-216. https://doi.org/10.4018/978-1-59140-848-2.ch009

APA

Davey, R. T., McCullagh, P. J., McAllister, H. G., & Houston, H. G. (2006). The Use of Artificial Neural Networks for Objective Determination of Hearing Threshold Using the Auditory Brainstem Response. In R. Begg, J. Kamruzzaman, & R. Sarker (Eds.), Neural Networks in Healthcare: Potential and Challenges (pp. 195-216). IGI Global. https://doi.org/10.4018/978-1-59140-848-2.ch009

Chicago

Davey, Robert T., et al. "The Use of Artificial Neural Networks for Objective Determination of Hearing Threshold Using the Auditory Brainstem Response." In Neural Networks in Healthcare: Potential and Challenges, edited by Rezaul Begg, Joarder Kamruzzaman, and Ruhul Sarker, 195-216. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-848-2.ch009

Export Reference

Mendeley
Favorite

Abstract

We have analyzed high and low level auditory brainstem response data (550 waveforms over a large age range; 126 were repeated sessions used in correlation analysis), by extracting time, frequency, and phase features and using these as inputs to ANN and decision tree classifiers. A two stage process is used. For responses with a high poststimulus to prestimulus power ratio indicative of high level responses, a classification accuracy of98% has been achieved. These responses are easily classified by the human expert. For lower level responses appropriate to hearing threshold, additional features from time, frequency, and phase have been used for classification, providing accuracies between 65% and 82%. These used a dataset with repeated recordings so that correlation could be employed. To increase classification accuracy, it may be necessary to combine the relevant features in a hybrid model.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.