Artificial Neural Network Analysis in the Evaluation of Ear Canal and Tympanic Membrane Properties from Acoustic Reflectometry Data

Abstract

Acoustic reflectometry (AR) can be applied to detect middle ear effusion (MEE) in order to diagnose otitis media with effusion (OME). Natural variation in the anatomy of the ear canal and tympanic membrane affects the result of AR. In the present study the effect of the length of the ear canal and the tension of the tympanic membrane on the results of AR was modelled. Six plastic models of the ear canal and tympanic membrane were constructed, with unique canal lengths and unique tympanic membrane tensions. The plastic models were measured with AR and the resulting data were analyzed with an artificial neural network (ANN) method. The results indicate that, with help of the ANN, the length of the ear canal and the tension of the tympanic membrane can be identified from AR data; in the validation phase the ANN classified the different ear canal lengths correctly in all six cases, and in five of the six cases it correctly classified the tympanic membrane tenseness. These test results may be useful when developing the AR method for more accurate diagnostics of OME.