Artificial Intelligence-Based Hearing Loss Detection Using Acoustic Threshold and Speech Perception Level

Hearing loss detection using automated audiometers and artificial intelligence methods has gained increasing attention in recent years. The proposed work aims: (a) to design an automated audiometer to diagnose hearing ability and to evaluate hearing intensity for healthy and profound hearing loss patients within 250 Hz to 8 kHz, (b) to compare the proposed automated audiometer with a conventional audiometer when estimating auditory perception level using pure tone and speech audiometers, and (c) to use the machine learning algorithms to classify hearing loss and normal subjects based on the selected features extracted from speech signals. Participants in the study included 50 healthy individuals and 50 patients with profound hearing loss. In the proposed hardware unit, the transmitted pure-tone signal and the speech signal stimulus are controlled automatically instead of being controlled manually. Using a digital potentiometer, a pure-tone audiometer can be automatically calibrated by varying the frequency and intensity of the generated tones according to the users' responses. During speech audiometric measurements, pre-recorded speech and repeated speech signals are analyzed to estimate speech recognition threshold (SRT) and word recognition score (WRS). The designed audiometer plots the audiogram automatically, estimating SRT and WRS, and classifying the subject as normal or hearing impaired. This study demonstrates the feasibility of using Machine Learning to predict hearing impairment in patients. A support vector machine, a random forest, and an AdaBoost model produced accuracy rates of 98%, 96%, and 96%, respectively, when identifying normal and hearing loss subjects. The proposed audiometer system is miniaturized, portable, and user-friendly in comparison to conventional audiometers. Consequently, the prototype would make it possible for subjects to conduct their own audiometric tests independently and send the results along with their audiogram to a trained medical professional to receive advice.