A machine learning approach for distinguishing hearing perception level using auditory evoked potentials

Abstract

An auditory loss is one of the most common disabilities present in newborns and infants in the world. A conventional hearing screening test's applicability is limited as it requires a feedback response from the subject under test. To overcome such problems, the primary focus of this study is to develop an intelligent hearing ability level assessment system using auditory evoked potential signals (AEP). AEP signal is a non-invasive tool that can reflect the stimulated interactions with neurons along the stations of the auditory pathway. The AEP responses of fourteen normal hearing subjects to auditory stimuli (20 dB, 30 dB, 40 dB, 50 dB and 60 dB) were derived from electroencephalogram (EEG) recordings. Higuchi's fractal method is applied to extract the fractal features from the recorded AEP signals. The extracted fractal features were then associated to different hearing perception levels of the subjects. Feed-forward and feedback neural networks are employed to distinguish the different hearing perception levels. The performance of the proposed intelligent hearing ability level assessment found to exceed 85% accuracy. This study indicates that AEP responses to the auditory stimuli to the normal hearing persons can predict the higher order auditory stimuli followed by the lower order auditory stimuli and consequently the state of auditory development of subjects