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Updated: May 13, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Advanced waveform decomposition for high-speed videoendoscopy analysis.

Takeshi Ikuma1, Melda Kunduk, Andrew J McWhorter

  • 1Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, Baton Rouge, LA 70809, USA. tikuma@ieee.org

Journal of Voice : Official Journal of the Voice Foundation
|March 16, 2013
PubMed
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This study introduces a new method to analyze nonperiodic vocal fold vibrations using high-speed videoendoscopy (HSV). The advanced waveform model and new HNR parameters effectively distinguish between healthy and pathological vocal folds.

Area of Science:

  • Biomedical Engineering
  • Acoustic Analysis
  • Vocal Fold Dynamics

Background:

  • High-speed videoendoscopy (HSV) captures vocal fold dynamics but its clinical use is limited.
  • Nonperiodic vocal fold behavior, crucial in pathological conditions, is not fully quantified by current methods.
  • Existing analysis of HSV data often overlooks complex nonperiodic patterns.

Purpose of the Study:

  • To develop and validate an advanced waveform modeling and decomposition technique for analyzing nonperiodic vocal fold behavior in HSV data.
  • To introduce novel harmonics-to-noise ratio (HNR) parameters for quantifying disordered vocal fold function.
  • To quantitatively assess the ability of the proposed method to differentiate between healthy and pathological vocal folds.

Main Methods:

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  • A novel waveform model decomposing HSV data into harmonic, deterministic nonharmonic, and random nonharmonic signal components.
  • Development of advanced HNR parameters, including harmonics-to-deterministic-noise ratio (HDNR) and harmonics-to-random-noise ratio.
  • Application of the model and parameters to glottal area waveforms from vocal folds with and without benign lesions.

    • Main Results:

    • The proposed advanced waveform model and HNR parameters successfully distinguished between vocal folds with and without benign lesions.
    • All three HNR parameters showed significant differences between the healthy and pathological groups.
    • The harmonics-to-deterministic-noise ratio (HDNR) demonstrated the largest effect size (Cohen's d = 2.04), indicating high sensitivity.

    Conclusions:

    • The novel waveform modeling and decomposition technique provides a robust method for analyzing nonperiodic vocal fold behavior from HSV data.
    • The developed HNR parameters, particularly HDNR, offer a sensitive quantitative measure for identifying vocal fold pathologies.
    • This approach enhances the clinical utility of HSV by enabling more precise characterization of voice disorders.