Complexity analysis of pathological voices by means of hidden Markov entropy measurements.
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.
Arias-Londoño
Julián D
JD
Digital Signal Processing Group, Universidad Nacional de Colombia sede Manizales, Colombia. jdariasl@unal.edu.co
Godino-Llorente
Juan I
JI
Castellanos-Domínguez
Germán
G
Sáenz-Lechon
Nicolás
N
Osma-Ruiz
Víctor
V
D000162:Acoustics; D000465:Algorithms; D001331:Automation; D001698:Biomedical Engineering / Q000379:methods; D019277:Entropy; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014831:Voice; D014832:Voice Disorders / Q000175:diagnosis / Q000503:physiopathology*