Objective dysphonia quantification in vocal fold paralysis: comparing nonlinear with classical measures

Clinical acoustic voice recording analysis is usually performed using classical perturbation measures including jitter, shimmer and noise-to-harmonic ratios. However, restrictive mathematical limitations of these measures prevent analysis for severely dysphonic voices. Previous studies of alternative nonlinear random measures addressed wide varieties of vocal pathologies. Here, we analyze a single vocal pathology cohort, testing the performance of these alternative measures alongside classical measures.

We present voice analysis pre- and post-operatively in unilateral vocal fold paralysis (UVFP) patients and healthy controls, patients undergoing standard medialisation thyroplasty surgery, using jitter, shimmer and noise-to-harmonic ratio (NHR), and nonlinear recurrence period density entropy (RPDE), detrended fluctuation analysis (DFA) and correlation dimension. Systematizing the preparative editing of the recordings, we found that the novel measures were more stable and hence reliable, than the classical measures, on healthy controls.

RPDE and jitter are sensitive to improvements pre- to post-operation. Shimmer, NHR and DFA showed no significant change (p > 0.05). All measures detect statistically significant and clinically important differences between controls and patients, both treated and untreated (p < 0.001, AUC > 0.7). Pre- to post-operation, GRBAS ratings show statistically significant and clinically important improvement in overall dysphonia grade (G) (AUC = 0.946, p < 0.001).

Re-calculating AUCs from other study data, we compare these results in terms of clinical importance. We conclude that, when preparative editing is systematized, nonlinear random measures may be useful UVFP treatment effectiveness monitoring tools, and there may be applications for other forms of dysphonia.
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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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Comparing aerosol number and mass exhalation rates from children and adults during breathing, speaking and singing

Aerosol particles of respirable size are exhaled when individuals breathe, speak and sing and can transmit respiratory pathogens between infected and susceptible individuals. The COVID-19 pandemic has brought into focus the need to improve the quantification of the particle number and mass exhalation rates as one route to provide estimates of viral shedding and the potential risk of transmission of viruses. Most previous studies have reported the number and mass concentrations of aerosol particles in an exhaled plume. We provide a robust assessment of the absolute particle number and mass exhalation rates from measurements of minute ventilation using a non-invasive Vyntus Hans Rudolf mask kit with straps housing a rotating vane spirometer along with measurements of the exhaled particle number concentrations and size distributions. Specifically, we report comparisons of the number and mass exhalation rates for children (12–14 years old) and adults (19–72 years old) when breathing, speaking and singing, which indicate that child and adult cohorts generate similar amounts of aerosol when performing the same activity. Mass exhalation rates are typically 0.002–0.02 ng s(−1) from breathing, 0.07–0.2 ng s(−1) from speaking (at 70–80 dBA) and 0.1–0.7 ng s(−1) from singing (at 70–80 dBA). The aerosol exhalation rate increases with increasing sound volume for both children and adults when both speaking and singing

The ocean sampling day consortium

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits

The Ocean Sampling Day Consortium

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits

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-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-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