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- W2297631020 abstract "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 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." @default.
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- W2297631020 date "2015-01-01" @default.
- W2297631020 modified "2023-10-16" @default.
- W2297631020 title "Maximum Approximate Entropy for Normal and Pathological Voices Classification" @default.
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- W2297631020 doi "https://doi.org/10.1007/978-3-319-13117-7_140" @default.
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