Jörg Lohscheller1, Ulrich Eysholdt. 1. Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Erlangen, Germany. joerg.lohscheller@uk-erlangen.de
Abstract
OBJECTIVES: High-speed (HS) video recordings are the up-to-date method for visualizing irregular vocal fold vibrations. However, perceptive evaluation during offline replay is time consuming and shows high inter-rater variability. METHOD: A visualization procedure is presented that extracts vocal fold vibrations from HS videos and transfers the motion information into a set of three phonovibrogram (PVG) images that make visual vocal fold displacements (PVG-0), velocities (PVG-1), and accelerations (PVG-2). RESULTS: The principles of PVG computation as well as their application to three clinical examples (normal voice, laryngeal nerve paralysis, functional voice disorder with vocal nodules) are presented. For normal and dysphonic subjects, the PVG images show the characteristics of vocal fold vibrations as concern the dynamic patterns of displacements, velocities, and accelerations. CONCLUSION: The PVG approach makes visual the entire range of motion of vibrating vocal fold edges in easy-to-read color images for differentiation of normal and pathologic voices. PVG images are printable and can be stored on a hard-disc drive, enabling the documentation of the course of voice disorders that is essential for evidenced-based medicine. PVG visualization has the potential to overcome the subjective quality of assessing HS videos, which makes it a valuable tool with broad clinical application.
OBJECTIVES: High-speed (HS) video recordings are the up-to-date method for visualizing irregular vocal fold vibrations. However, perceptive evaluation during offline replay is time consuming and shows high inter-rater variability. METHOD: A visualization procedure is presented that extracts vocal fold vibrations from HS videos and transfers the motion information into a set of three phonovibrogram (PVG) images that make visual vocal fold displacements (PVG-0), velocities (PVG-1), and accelerations (PVG-2). RESULTS: The principles of PVG computation as well as their application to three clinical examples (normal voice, laryngeal nerve paralysis, functional voice disorder with vocal nodules) are presented. For normal and dysphonic subjects, the PVG images show the characteristics of vocal fold vibrations as concern the dynamic patterns of displacements, velocities, and accelerations. CONCLUSION: The PVG approach makes visual the entire range of motion of vibrating vocal fold edges in easy-to-read color images for differentiation of normal and pathologic voices. PVG images are printable and can be stored on a hard-disc drive, enabling the documentation of the course of voice disorders that is essential for evidenced-based medicine. PVG visualization has the potential to overcome the subjective quality of assessing HS videos, which makes it a valuable tool with broad clinical application.
Authors: Daryush D Mehta; Dimitar D Deliyski; Thomas F Quatieri; Robert E Hillman Journal: J Speech Lang Hear Res Date: 2010-08-10 Impact factor: 2.297
Authors: Maria E Powell; Dimitar D Deliyski; Steven M Zeitels; James A Burns; Robert E Hillman; Terri Treman Gerlach; Daryush D Mehta Journal: J Voice Date: 2019-04-17 Impact factor: 2.009
Authors: Anxiong Yang; Jörg Lohscheller; David A Berry; Stefan Becker; Ulrich Eysholdt; Daniel Voigt; Michael Döllinger Journal: J Acoust Soc Am Date: 2010-02 Impact factor: 1.840