Classification of Brainstem Auditory Evoked Potentials Using Artificial Neural Network Based on Time and Frequency Domain Features

Abstract

This article presents a novel technique of automated classification of brainstem auditory evoked potentials (BAEPs). Brainstem auditory evoked potentials are the evoked potentials used to measure the brain activity that occur in response to the auditory stimulus to the ear and to record the response by placing electrodes on the surface of the scalp. Brainstem auditory evoked potentials reflect neuronal activity in the auditory nerve pathway from the cochlea to the auditory cortex and are used in the diagnosis of neurological diseases leading to hearing loss. In the proposed automated system, the features of BAEPs are extracted in time domain based on latency, and in frequency domain, using fast Fourier transform and discrete wavelet transform. The features extracted from BAEPs are used as variables to build a feed-forward multilayer perceptron, an artificial neural network model for the classification of BAEPs. The stratified 10-fold cross-validation technique is used in training and testing the model to estimate the performance of the system. The combined time and frequency domain features along with physiological parameters as input to artificial neural network resulted in a very good average accuracy of 90.74% in classifying the normal and abnormal BAEP signals.