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High-speed image analysis reveals chaotic vibratory behaviors of pathological vocal folds

Author

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  • Zhang, Yu
  • Shao, Jun
  • Krausert, Christopher R.
  • Zhang, Sai
  • Jiang, Jack J.

Abstract

Laryngeal pathology is usually associated with irregular dynamics of laryngeal activity. High-speed imaging facilitates direct observation and measurement of vocal fold vibrations. However, chaotic dynamic characteristics of aperiodic high-speed image data have not yet been investigated in previous studies. In this paper, we will apply nonlinear dynamic analysis and traditional perturbation methods to quantify high-speed image data from normal subjects and patients with various laryngeal pathologies including vocal fold nodules, polyps, bleeding, and polypoid degeneration. The results reveal the low-dimensional dynamic characteristics of human glottal area data. In comparison to periodic glottal area series from a normal subject, aperiodic glottal area series from pathological subjects show complex reconstructed phase space, fractal dimension, and positive Lyapunov exponents. The estimated positive Lyapunov exponents provide the direct evidence of chaos in pathological human vocal folds from high-speed digital imaging. Furthermore, significant differences between the normal and pathological groups are investigated for nonlinear dynamic and perturbation analyses. Jitter in the pathological group is significantly higher than in the normal group, but shimmer does not show such a difference. This finding suggests that the traditional perturbation analysis should be cautiously applied to high speed image signals. However, the correlation dimension and the maximal Lyapunov exponent reveal a statistically significant difference between normal and pathological groups. Nonlinear dynamic analysis is capable of quantitatively describing the aperiodic vocal fold vibrations and may be helpful for understanding disordered behaviors in pathological laryngeal systems.

Suggested Citation

  • Zhang, Yu & Shao, Jun & Krausert, Christopher R. & Zhang, Sai & Jiang, Jack J., 2011. "High-speed image analysis reveals chaotic vibratory behaviors of pathological vocal folds," Chaos, Solitons & Fractals, Elsevier, vol. 44(1), pages 169-177.
  • Handle: RePEc:eee:chsofr:v:44:y:2011:i:1:p:169-177
    DOI: 10.1016/j.chaos.2011.01.007
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    Cited by:

    1. Lahmiri, Salim, 2017. "On fractality and chaos in Moroccan family business stock returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 29-39.
    2. Zhang, Yu & Sprecher, Alicia J. & Zhao, ZongXi & Jiang, Jack J., 2011. "Nonlinear detection of disordered voice productions from short time series based on a Volterra–Wiener–Korenberg model," Chaos, Solitons & Fractals, Elsevier, vol. 44(9), pages 751-758.
    3. Lahmiri, Salim, 2017. "Investigating existence of chaos in short and long term dynamics of Moroccan exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 655-661.
    4. Lahmiri, Salim, 2017. "A study on chaos in crude oil markets before and after 2008 international financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 389-395.

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