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Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model

1
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2
Department of Mechanical and Aeronautical Engineering, Clarkson University, Potsdam, NY 13699, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2735; https://doi.org/10.3390/app9132735
Received: 7 May 2019 / Revised: 1 July 2019 / Accepted: 3 July 2019 / Published: 6 July 2019
(This article belongs to the Special Issue Computational Methods and Engineering Solutions to Voice)
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Abstract

Bayesian estimation has been previously demonstrated as a viable method for developing subject-specific vocal fold models from observations of the glottal area waveform. These prior efforts, however, have been restricted to lumped-element fitting models and synthetic observation data. The indirect relationship between the lumped-element parameters and physical tissue properties renders extracting the latter from the former difficult. Herein we propose a finite element fitting model, which treats the vocal folds as a viscoelastic deformable body comprised of three layers. Using the glottal area waveforms generated by self-oscillating silicone vocal folds we directly estimate the elastic moduli, density, and other material properties of the silicone folds using a Bayesian importance sampling approach. Estimated material properties agree with the “ground truth” experimental values to within 3 % for most parameters. By considering cases with varying subglottal pressure and medial compression we demonstrate that the finite element model coupled with Bayesian estimation is sufficiently sensitive to distinguish between experimental configurations. Additional information not available experimentally, namely, contact pressures, are extracted from the developed finite element models. The contact pressures are found to increase with medial compression and subglottal pressure, in agreement with expectation. View Full-Text
Keywords: patient-specific modeling; silicone vocal fold models; Bayesian inverse analysis; finite element analysis patient-specific modeling; silicone vocal fold models; Bayesian inverse analysis; finite element analysis
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Hadwin, P.J.; Motie-Shirazi, M.; Erath, B.D.; Peterson, S.D. Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model. Appl. Sci. 2019, 9, 2735.

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