Complexity analysis of pathological voices by means of hidden markov entropy measurements

Author

Arias-Londoño, Julián D. ; Godino-Llorente, Juan I. ; Castellanos-Domínguez, Germán ; Sáenz-Lechón, Nicolás ; Osma-Ruiz, Víctor

Author_Institution

Digital Signal Process. Group, Univ. Nac. de Colombia sede Manizales, Manizales, Colombia

fYear

2009

fDate

3-6 Sept. 2009

Firstpage

2248

Lastpage

2251

Abstract

In this work an entropy based nonlinear analysis of pathological voices is presented. The complexity analysis is carried out by means of six different entropies, including three measures derived from the entropy rate of Markov chains. The aim is to characterize the divergence of the trajectories and theirs directions into the state space of Markov chains. By employing these measures in conjunction with conventional entropy features, it is possible to improve the discrimination capabilities of the nonlinear analysis in the automatic detection of pathological voices.

Keywords

diseases; entropy; hidden Markov models; medical signal detection; medical signal processing; speech; speech processing; Markov chains; automatic pathological voice detection; complexity analysis; entropy-based nonlinear analysis; hidden Markov entropy measurement; state space method; Acoustics; Algorithms; Automation; Biomedical Engineering; Entropy; Humans; Markov Chains; Models, Statistical; Pattern Recognition, Automated; ROC Curve; Signal Processing, Computer-Assisted; Time Factors; Voice; Voice Disorders;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Conference_Location

Minneapolis, MN

ISSN

1557-170X

Print_ISBN

978-1-4244-3296-7

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2009.5334996

Filename

5334996