Nonlinear Dynamic Analysis and Perturbation Measurement Used for Discriminating Pathological Voices and Their Correlations With Perceptual Evaluation

• Published in: Journal of voice
• Main Authors: Zhang, Fan, Liu, Boquan, Wu, Pinhong, Johnson, Colbey, Bouharati, Melissa, Zhang, Yi, He, Peijie, Jiang, Jack J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 07.03.2024
• Subjects: Acoustic analysis; Correlations; Non-linear dynamic analysis; Otolaryngology; Pathological voice; Perceptual evaluation; Perturbation measurement; Pathological voice; Correlations; Acoustic analysis; Perceptual evaluation; Non-linear dynamic analysis; Perturbation measurement
• ISSN: 0892-1997; 1873-4588; 1873-4588
• DOI: 10.1016/j.jvoice.2023.10.010

1. To investigate the discriminatory and diagnostic power of nonlinear dynamic analysis measures concerning voices from normal, benign, and malignant voice disorders. 2. To study the correlations of nonlinear dynamic analysis measures with perceptual ratings to evaluate the reliability of the objective acoustic analysis in predicting severity of voice. The perturbation analysis metrics used were Jitter%, Shimmer%, and signal-to-noise ratio. The nonlinear dynamic analysis metrics used were spectrum convergence ratio (SCR), nonlinear energy difference ratio (NEDR), and rate of divergence (ROD). Subjects were enrolled and divided into three groups based on laryngeal pathology: normal, benign, and malignant. Vowel sound and reading samples were recorded. Perceptual evaluation was applied to these voice samples to investigate correlations between metrics and auditory perception. Each metric was capable of discriminating laryngeal pathology, except for SCR in the case of distinguishing between benign and malignant pathologies. Perturbation analysis parameters had a moderate ability to differentiate between normal and benign pathologies, but were unable to characterize malignant pathologies for certain diseases, such as Reinke’s edema. All metrics significantly correlated with perceptual G scores. Nonlinear dynamic analysis was superior when applied to cases of severe dysphonia, where linear metrics such as Jitter% and Shimmer% tended to lose utility. NEDR and ROD were successful at differentiating between the different pathologies, whereas SCR could not discriminate between the benign and malignant groups. Perturbation and nonlinear dynamic analyses are comparable in their discriminating power with respect to normal and benign voices, and normal and malignant voices. The nonlinear dynamic analysis metrics NEDR and ROD may be superior in clinical settings with respect to discriminating voice pathology ranging from mild pathological voice to severe dysphonia, and with respect to discriminating benign and malignant voice. SCR was found unable to discriminate pathological voices.