Literature DB >> 19502002

Nonlinear dynamic analysis of disordered voice: the relationship between the correlation dimension (D2) and pre-/post-treatment change in perceived dysphonia severity.

Shaheen N Awan1, Nelson Roy, Jack J Jiang.   

Abstract

The purpose of this study was to evaluate the clinical utility of nonlinear dynamic analysis methods, including phase space portraits and measures of the correlation dimension (D(2)) to predict pre- versus post-treatment change in perceived dysphonia severity in a group of 88 patients with muscle tension dysphonia (MTD). Pre- and posttreatment vowel samples from 88 women with primary MTD (mean age=46.2 years; standard deviation=13.1) were selected for analysis (176 voice samples in total). Phase space reconstructions and correlation dimensions were computed to describe the nonlinear dynamic characteristics of all voice samples. Ten blinded listeners were asked to rate the vowel samples for severity of dysphonia using a 100-point visual analog scale (VAS). In the computation of D(2) results, 22 severely dysphonic pretreatment voice samples were not analyzed, as a finite value for the correlation dimension could not be computed. For the remaining pre-/post-treatment voice samples, a significant difference in the correlation dimension (D(2)) between the pre- versus post-treatment voice samples was observed; however, D(2) was poorly correlated with changes in perceived dysphonia severity ratings after treatment (r=0.244, P=0.056). Thus, the utility of the correlation dimension (D(2)) as a treatment-outcome measure and as a measure of dysphonia that may strongly relate to perceived dysphonia severity does not appear to be supported, particularly for pretreatment voices that may have increased levels of dysphonia severity. Instead, the strength of nonlinear dynamic methods may potentially reside in providing some insight into the theoretical rules or initial conditions that may result in different modes of normal or disordered phonation. (c) 2010 The Voice Foundation. Published by Mosby, Inc. All rights reserved.

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Year:  2009        PMID: 19502002     DOI: 10.1016/j.jvoice.2008.11.003

Source DB:  PubMed          Journal:  J Voice        ISSN: 0892-1997            Impact factor:   2.009


  6 in total

1.  Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods.

Authors:  Boquan Liu; Evan Polce; Julien C Sprott; Jack J Jiang
Journal:  J Speech Lang Hear Res       Date:  2018-05-17       Impact factor: 2.297

2.  Using Rate of Divergence as an Objective Measure to Differentiate between Voice Signal Types Based on the Amount of Disorder in the Signal.

Authors:  William M Calawerts; Liyu Lin; J C Sprott; Jack J Jiang
Journal:  J Voice       Date:  2016-02-23       Impact factor: 2.009

3.  Nonlinear analyses of elicited modal, raised, and pressed rabbit phonation.

Authors:  Shaheen N Awan; Carolyn K Novaleski; Bernard Rousseau
Journal:  J Voice       Date:  2014-05-16       Impact factor: 2.009

4.  Evaluating the Voice Type Component Distributions of Excised Larynx Phonations at Three Subglottal Pressures.

Authors:  Boquan Liu; Hayley Raj; Logan Klein; Jack J Jiang
Journal:  J Speech Lang Hear Res       Date:  2021-04-22       Impact factor: 2.297

5.  Chaos Behavior Analysis of Alaryngeal Voices Including Esophageal (SE) and Tracheoesophageal (TE) Voices.

Authors:  Boquan Liu; Fan Zhang; Ling Chen; Matthew A Silverman; Hengxin Liu; Dehui Fu; Yongwang Huang; Jing Pan; Jack J Jiang
Journal:  Folia Phoniatr Logop       Date:  2022-01-20       Impact factor: 1.391

Review 6.  Functional dysphonia: strategies to improve patient outcomes.

Authors:  Mara Behlau; Glaucya Madazio; Gisele Oliveira
Journal:  Patient Relat Outcome Meas       Date:  2015-12-01
  6 in total

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