Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection |
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| Authors: | Max A Little Patrick E McSharry Stephen J Roberts Declan AE Costello Irene M Moroz |
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| Institution: | (1) Systems Analysis, Modelling and Prediction Group, Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ Oxford, UK;(2) Pattern Analysis Research Group, Department of Engineering Science, University of Oxford, Parks Road, Oxford, UK;(3) Applied Dynamical Systems Research Group, Oxford Centre for Industrial and Applied Mathematics, Mathematics Institute, University of Oxford, OX1 3JP Oxford, UK;(4) Milton Keynes General Hospital, Standing Way, Eaglestone, MK6 5LD Milton Keynes, Bucks, UK |
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| Abstract: | 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. |
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