Auditory Evoked Potential-Based Hearing Loss Level Recognition Using Fully Convolutional Neural Networks

Abstract

Hearing perception loss is the main common disabilities existing in adults confirmed by the auditory evoked potential exam (AEP). This technique is characterized by limited medical information from feedback response in full routine examination of patients. Body movements, measuring equipment, low-frequency noise are outside factors that cause a misinterpretation. In clinical workflow, AEP signals are manually classified by the experts in order to precise the hearing loss level. In order to enhancethe diagnosis rung rightness, the fully convolutional neural networks methodology is proposed to highlight reliably automated hearing loss analysis. The validation of the proposed approach was focused on 494 factual incorporated auditory loss cases and 177 seen normal undergoing different auditory stimuli (20 dB, 50, 60... and 80 dB) from AEP recordings. The used classification method can represent a highly reduced labor-intensive study loads of ear nose throat (ENT) doctor by applying the pertinent analysis strategy for each hearing loss level and significantly increase the auditory diagnosis performance which provides ability for a computerized ENT assessment. Compared to state-of-the-art methods, the used technique presents a higher accuracy rate by requiring hearing loss level classes.