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Original Research
Predicting hearing loss from otoacoustic emissions using an artificial neural network
South African Journal of Communication Disorders | Vol 49, No 1 | a215 |
DOI: https://doi.org/10.4102/sajcd.v49i1.215
| © 2019 Rouviere de Waal, René Hugo, Maggi Soer, Johann J. Krüger
| This work is licensed under CC Attribution 4.0
Submitted: 11 October 2016 | Published: 31 December 2002
Submitted: 11 October 2016 | Published: 31 December 2002
About the author(s)
Rouviere de Waal, Department of Communication Pathology and Electronic Engineering, University of Pretoria, South AfricaRené Hugo, Department of Communication Pathology and Electronic Engineering, University of Pretoria, South Africa
Maggi Soer, Department of Communication Pathology and Electronic Engineering, University of Pretoria, South Africa
Johann J. Krüger, Department of Communication Pathology and Electronic Engineering, University of Pretoria, South Africa
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PDF (360KB)Abstract
Normal and impaired pure tone thresholds (PTTs) were predicted from distortion product otoacoustic emissions (DP using a feed-forward artificial neural network (ANN) with a back-propagation training algorithm. The ANN used a present and absent DPOAEs from eight DP grams, (2fl -f2 = 406 - 4031 Hz) to predict PTTs at 0.5, 1, 2 and 4 kHz. With normal hearing as < 25 dB HL, prediction accuracy of normal hearing was 94% at 500, 88% at 1000, 88% at 2000 and 93% at 4000 Hz. Prediction of hearing-impaired categories was less accurate, due to insufficient data for the ANN to train on. This research indicates the possibility of accurately predicting hearing ability within 10 dB in normal hearing individuals and in hearing-impaired listeners with DPOAEs and ANNsfrom 500 - 4000 Hz.
Keywords
otoacoustic emissions; distortion product otoacoustic emissions; artificial neural networks; hearing threshold prediction; objective hearing assessment
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