Objective prediction of pure tone thresholds in normal and hearing-impaired ears with distortion product otoacoustic emissions and artificial neural networks

• Main Author: De Waal, Rouviere
• Other Authors: Prof S R Hugo
• Published: 2013
• Subjects: Hearing; Audiometry; Hearing disorders; Otoacoustic emissions testing; Hearing impaired; Neural networks (computer science); UCTD
• Online Access: http://hdl.handle.net/2263/26276; De Waal, R 2000 Pure tone thresholds in normal and hearing-impaired ears with distortion product otoacoustic emissions and artificial neural networks, DPhil thesis, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/26276 >; http://upetd.up.ac.za/thesis/available/etd-07142006-112943/

In the evaluation of special populations, such as neonates, infants and malingerers, audiologists have to rely heavily on objective measurements to assess hearing ability. Current objective audiological procedures such as tympanometry, the acoustic reflex, auditory brainstem response and transient evoked otoacoustic emissions, however, have certain limitations, contributing to the need of an objective, non-invasive, rapid, economic test of hearing that evaluate hearing ability in a wide range of frequencies. The purpose of this study was to investigate distortion product otoacoustic emissions (DPOAEs) as an objective test of hearing. The main aim was to improve prediction of pure tone thresholds at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz with DPOAEs and artificial neural networks (ANNs) in normal and hearing-impaired ears. Other studies that attempted to predict hearing ability with DPOAEs and conventional statistical methods were only able to distinguish between normal and impaired hearing. Back propagation neural networks were trained with the pattern of all present and absent DPOAE responses of 11 DPOAE frequencies of eight DP Grams and pure tone thresholds at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz. The neural network used the learned correlation between these two data sets to predict hearing ability at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz. Hearing ability was not predicted as a decibel value, but into one of several categories spanning 1 OdB. Results for prediction accuracy of normal hearing improved from 92% to 94% at 500 Hz, 87% to 88% at 1000 Hz, 84% to 88% at 2000 Hz and 91% to 93% at 4000 Hz from the De Waal (1998) study to the present study. The improvement of prediction of normal hearing can be attributed to extensive experimentation with neural network topology and manipulation of input data to present information to the network optimally. The prediction of hearing-impaired categories was less satisfactory, due to insufficient data for the ANNs to train on. A prediction versus ear count correlation strongly suggested that the inaccurate predictions of hearing-impaired categories is not a result of an inability of DPOAEs to predict pure tone thresholds in hearing impaired ears, but a result of insufficient data for the neural network to train on. This research concluded that DPOAEs and ANNs can be used to accurately predict hearing ability within 10dB in normal and hearing-impaired ears from 500 Hz to 4000 Hz for hearing losses of up to 65dB HL. === Thesis (DPhil (Communication Pathology))--University of Pretoria, 2007. === Speech-Language Pathology and Audiology === unrestricted