Fuzzy Clustering of Transient Evoked OtoAcoustic Emission Signals Using Gustafson Kessel Algorithm

Abstract

Healthy ears reflect OtoAcoustic Emission (OAE) signals in response to acoustic stimuli that can be recorded in the external ear canal. We can use the existence of this signal to check the health condition of hearing. Early diagnosis and treatment of hearing abnormalities of infants can save them from losing their hearing. In this paper we use a dataset which consists of OAE signals of subjects with normal and abnormal hearing. First fundamental features which are responsible for the existence of OAE are extracted from unlabeled data, and then Gustafson Kessel clustering Algorithm is applied to the feature space to classify them in two clusters. After that we allocate each labeled data to these clusters based on their distance from cluster centers. Finally we label each cluster based on majority voting rule for their nearest neighbors which are labeled. The best result is 96.6% of accuracy that is achieved from executing the algorithm in 20 trials. Checking the health condition of hearing using OAE signals for experts usually is an error-prone diagnosis, thus to reach an accurate diagnosis, additional tests such as audiometry and tympanometry are commonly used. These tests are time consuming and costly. Reducing these disadvantages leads us to automate diagnosis as a supporting tool to help the experts.