The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions

Abstract

In the evaluation of special populations, such as neonates, infants and malingerers, audiologist often 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 attempt to predict hearing ability 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 10-15dB. Results indicated that prediction accuracy of normal hearing was 92% at 500 Hz, 87% at 1000 Hz, 84% at 2000 Hz and 91% at 4000 Hz. The prediction of hearing-impaired categories was less satisfactory, due to insufficient data for the ANNs to train on. The variables age and gender were included in some of the neural network runs to determine their effect on the distortion product. Gender had only a minor positive effect on prediction accuracy, but age affected prediction accuracy considerably in a positive way. The effect of the amount of data that the neural network had to train on was also investigated. 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.