Classification of glottic insufficiency and tension asymmetry using a multilayer perceptron.

Abstract

Laryngeal function can be evaluated from multiple perspectives, including aerodynamic input, acoustic output, and mucosal wave vibratory characteristics. To determine the classifying power of each of these, we used a multilayer perceptron artificial neural network (ANN) to classify data as normal, glottic insufficiency, or tension asymmetry. Case series analyzing data obtained from excised larynges simulating different conditions. Aerodynamic, acoustic, and videokymographic data were collected from excised canine larynges simulating normal, glottic insufficiency, and tension asymmetry. Classification of samples was performed using a multilayer perceptron ANN. A classification accuracy of 84% was achieved when including all parameters. Classification accuracy dropped below 75% when using only aerodynamic or acoustic parameters and below 65% when using only videokymographic parameters. Samples were classified with the greatest accuracy when using a wide range of parameters. Decreased classification accuracies for individual groups of parameters demonstrate the importance of a comprehensive voice assessment when evaluating dysphonia.