Maximum approximate entropy for normal and pathological voices classification

JUAN FELIPE RESTREPO; GASTON SCHLOTTHAUER; MARÍA EUGENIA TORRES

Paraná

Congreso; VI Latin American Congress on Biomedical Engineering CLAIB 2014; 2014

The assessment of voice signals using non-linear features has proved to be valuable tool for the automatic detection of pathological voices. In this paper we propose a new approach based on the maximum approximate entropy estimator (ApEnmax) and the r value at which it is achieved (rmax). Through experiments with data from the MEEI voice disorders database, we evaluate the proficiency of these estimators as a function of the embedding dimension m. Using these features along with linear discriminant analysis and principal component analysis, we have achieved an accuracy of 94.6%, a sensibility of 98.2% and a specificity of 84.7%. We can conclude that the jointly use of ApEnmax and rmax can be used to discriminate between pathological and normal voices. Moreover, the discrimination capacity of a simple linear classifier can be increased using in conjunction the information brought by these estimators through several m values.