Evaluating Laryngeal Stability in Drug-Induced Parkinsonism Using Hilbert-Huang Transform and Chaos Dynamics: A...

Background/Objectives: Drug-induced parkinsonism (DIP) is a common extrapyramidal adverse effect of dopamine-receptor blocking agents, yet interpretable biomarkers of its acoustics remain limited. This study investigated whether nonlinear dynamics could provide physiologically interpretable markers of DIP and clarify how these abnormalities vary across vowels and extrapyramidal symptoms. Methods: Sustained phonations of five vowels (/a/, /i/, /u/, /e/, /o/) were analyzed in 82 patients with DIP and 30 healthy controls. Hilbert-Huang Transform (HHT) features were integrated along with Sample Entropy, Correlation Dimension, Largest Lyapunov Exponent, and Recurrence Quantification Analysis (RQA). Associations with Drug-Induced Extrapyramidal Symptoms Scale (DIEPSS) domains were examined. An external idiopathic Parkinson's disease (IPD) dataset and machine learning were used as secondary analyses to evaluate diagnostic utility. Results: HHT-derived features showed vowel dependence, whereas nonlinear measures revealed broader abnormalities in DIP. Sample Entropy and the Correlation Dimension were most prominently increased in rounded back vowels /u/ and /o/. RQA determinism was significantly reduced across all vowels, with recurrence rate and laminarity reduced across multiple vowels, indicating impaired recurrence of phonation. Within the DIP cohort, dyskinesia showed the strongest association with acoustic abnormalities, whereas broader motor domains, including Gait, Bradykinesia, Rigidity, Tremor, Dyskinesia, and Overall Severity, characterized patient-control differences. Secondary analyses with Random Forest achieved a three-class classification of 76.0% (CI 70.3-81.3%). Conclusions: DIP is associated with measurable disruption of nonlinear vocal dynamics, characterized by vowel-sensitive HHT and entropy features and relatively consistent reductions in RQA determinism. These interpretable acoustic patterns may provide a non-invasive means of characterizing extrapyramidal motor dysfunction.