DESIGN AND IMPLEMENTATION OF A PORTABLE AUTOMATED AUDIOMETER FOR HEARING CLASSIFICATION USING MACHINE LEARNING APPROACHES

Abstract

Audiometric tests can identify the hearing loss at specific frequencies using the audiogram. The aim and objectives of the study were (i) to develop an automated audiometer for self-diagnosing the hearing ability of the patient; (ii) to extract the features from the acoustic signals and to classify the normal and profound hearing loss patients using different machine learning algorithms; (iii) to validate the hearing loss classification using six-frequency average (6-FA) method based on simple linear regression analysis and machine learning algorithms. The study is conducted among 150 patients, including 75 patients with normal hearing ability and 75 patients with profound hearing loss. The total population of 150 underwent audiometric test both in the soundproof audiometric room and in the normal field environment. Based on the patient response, the intensity and frequency are changed automatically, and the audiogram is plotted by the principle of Artificial Neural Network learning procedures. The overall accuracy produced by classification of normal and profound hearing loss patients using Support Vector Machine (SVM), k-Nearest Neighbor classifier, and Naïve Bayes classifier is 97%, 96%, and 95%, respectively. The results indicated that the SVM classifier outperforms the other two classifiers well. The preliminary audiometric test can be performed remotely and then consulted with an audiologist. Thus, the patient could operate the developed prototype independently and get a consultation from trained medical personnel.