Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection-5

<p><b>Copyright information:</b></p><p>Taken from "Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection"</p><p>http://www.biomedical-engineering-online.com/content/6/1/23</p><p>BioMedical Engineering OnLine 2007;6():23-23.</p><p>Published online 26 Jun 2007</p><p>PMCID:PMC1913514.</p><p></p>signal from the Kay Elernetrics database. For clarity only a small section is shown (1500 samples)

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection-3

<p><b>Copyright information:</b></p><p>Taken from "Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection"</p><p>http://www.biomedical-engineering-online.com/content/6/1/23</p><p>BioMedical Engineering OnLine 2007;6():23-23.</p><p>Published online 26 Jun 2007</p><p>PMCID:PMC1913514.</p><p></p> as shown in figure 1. (a) Normal voice (JMC1NAL), (b) disordered voice (JXS01AN). The values of the recurrence analysis parameters were the same as those in the analysis of figure 3. The normalised RPDE value is larger for the disordered voice

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection-2

<p><b>Copyright information:</b></p><p>Taken from "Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection"</p><p>http://www.biomedical-engineering-online.com/content/6/1/23</p><p>BioMedical Engineering OnLine 2007;6():23-23.</p><p>Published online 26 Jun 2007</p><p>PMCID:PMC1913514.</p><p></p>tly periodic signal (a) created by taking a single cycle (period = 134 samples) from a speech signal and repeating it end-to-end many times. The signal was normalised to the range [-1, 1]. (b) All values of () are zero except for (133) = 0.1354 and (134) = 0.8646 so that () is properly normalised. This analysis is also applied to (c) a synthesised, uniform i.i.d. random signal on the range [-1, 1], for which (d) the density () is fairly uniform. For clarity only a small section of the time series (1000 samples) and the recurrence time (1000 samples) is shown. Here, = 1000. The length of both signals was 18088 samples. The optimal values of the recurrence analysis parameters were found at = 0.12, = 4 and = 35

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection-6

<p><b>Copyright information:</b></p><p>Taken from "Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection"</p><p>http://www.biomedical-engineering-online.com/content/6/1/23</p><p>BioMedical Engineering OnLine 2007;6():23-23.</p><p>Published online 26 Jun 2007</p><p>PMCID:PMC1913514.</p><p></p>ed (JXS01AN) speech signal from the Kay Elernetrics database. For clarity only a small section is shown (1500 samples). The embedding dimension is = 3 and the time delay is = 7 samples

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection-0

<p><b>Copyright information:</b></p><p>Taken from "Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection"</p><p>http://www.biomedical-engineering-online.com/content/6/1/23</p><p>BioMedical Engineering OnLine 2007;6():23-23.</p><p>Published online 26 Jun 2007</p><p>PMCID:PMC1913514.</p><p></p>signal from the Kay Elernetrics database. For clarity only a small section is shown (1500 samples)

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection-4

<p><b>Copyright information:</b></p><p>Taken from "Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection"</p><p>http://www.biomedical-engineering-online.com/content/6/1/23</p><p>BioMedical Engineering OnLine 2007;6():23-23.</p><p>Published online 26 Jun 2007</p><p>PMCID:PMC1913514.</p><p></p>base. (a) Normal voice (GPG1NAL) signal, (c) disordered voice (RWR14AN). Discrete-time signals shown over a limited range of for clarity. (b) Logarithm of scaling window sizes against the logarithm of fluctuation size () for normal voice in (a). (d) Logarithm of scaling window sizes against the logarithm of fluctuation size () for disordered voice in (b). The values of ranged from = 50 to = 100 in steps of five. In (b) and (d), the dotted line is the straight-line fit to the logarithms of the values of and () (black dots). The values of and the normalised version show an increase for the disordered voice

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection-1

<p><b>Copyright information:</b></p><p>Taken from "Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection"</p><p>http://www.biomedical-engineering-online.com/content/6/1/23</p><p>BioMedical Engineering OnLine 2007;6():23-23.</p><p>Published online 26 Jun 2007</p><p>PMCID:PMC1913514.</p><p></p>ed (JXS01AN) speech signal from the Kay Elernetrics database. For clarity only a small section is shown (1500 samples). The embedding dimension is = 3 and the time delay is = 7 samples