EEG based hearing perception level classification using fractal features

Abstract

The primary focus of this study is to develop a hearing ability level assessment system using auditory evoked potential signals (AEP). AEP signal is a non-invasive tool that provides auditory pathway information content and its stimulated interactions with neurons. To record the hearing perception levels at different sound intensity levels, namely, 20 dB, 30 dB, 40 dB, 50 dB and 60 dB in both the ears of a normal subject, a simple AEP based hearing perception level protocol has been proposed. The detrended fluctuation analysis (DFA) has been used to estimate the fractal values of different hearing perception levels of the recorded AEP signals. The extracted fractal features were then associated to different hearing perception levels of a subject and neural network models were developed. The maximum classification accuracy of the developed neural network models for the left and right ears are observed as 78.57% and 80.71% respectively. From the classification accuracy, it has been inferred that the neural network models are able to discriminate the five distinct hearing perception levels of a normal hearing person.