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

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 of IDEA GROUP PUBLISHING This paper appears in the publication, Neural Networks in Healthcare: Potential and Challenges edited by Rezaul Begg, Joarder Kamruzzaman, and Ruhul Sarker © 2006, Idea Group Inc. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB12181 196 Davey, McCullagh, McAllister, & Houston Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 98% 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.