Fuzzy Clustering of Transient Evoked OtoAcoustic Emission Signals based on K-Nearest Neighbours Rule

M.N. Hosseini, A.H. Jafari, S. Fazeli, and S. Mahmoudian (Iran)


OtoAcoustic Emissions, Transient Evoked OtoAcoustic Emissions, Fuzzy clustering, K-nearest neighbour rule.


Every healthy ear reflects a signal (OtoAcoustic Emission, OAE, signals) in response to a stimulus, so we can check the health condition of hearing based on the existence of this signal. If the hearing abnormality of neonates is diagnosed in the early month of their birth, with early treatment we can save them from losing their hearing. In this work we use a data set recorded from subjects with normal and abnormal hearing. First, basic features responsible for the existence of OAE are extracted from unlabeled data, and then KNN-FCM Algorithm is applied to the feature space to classify them in to two clusters. After that we illustrate that each labelled data belongs to which of the clusters based on their distance from cluster centers. Finally we label each cluster applying majority voting rule for those nearest neighbors that 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 are commonly used. These tests maybe time-consuming and costly, reducing these disadvantages leads us to this research to automate diagnosis as a supporting tool to help the experts.

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