Predicting hearing loss from otoacoustic emissions using an artificial neural network

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.