High-speed image analysis reveals chaotic vibratory behaviors of pathological vocal folds - Zhang, Yu; Shao, Jun; Krausert, Christopher R.; Zhang, Sai; Jiang, Jack J. (Chaos, solitons and fractals, 2011)
► Low-dimensional human glottal area data. ► Evidence of chaos in human laryngeal activity from high-speed digital imaging. ► Traditional perturbation analysis should be cautiously applied to aperiodic high speed image signals. ► Nonlinear dynamic analysis may be helpful for understanding disordered behaviors in pathological laryngeal systems. 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.