Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection

Background: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.

Methods: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.

Results: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.

Conclusions: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.
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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-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

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-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