利用希尔伯特-黄变换与混沌动力学评估药物诱发性帕金森综合征的喉部稳定性：一种用于鉴别诊断的多元音非线性声学框架
Evaluating Laryngeal Stability in Drug-Induced Parkinsonism Using Hilbert-Huang Transform and Chaos Dynamics: A Multi-Vowel Nonlinear Acoustic Framework for Differential Diagnosis.
文献信息
| PMID | 42882342 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Andrew An-Zhe Lee |
| 作者单位 | School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan. |
| 期刊 | Diagnostics (Basel, Switzerland) |
| SCI 分区 | Q1 |
| IF | 3.6 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 咽喉科 |
中文摘要
背景/目的： 药物诱发性帕金森综合征（DIP）是多巴胺受体阻断剂常见的锥体外系不良反应，但其声学上可解释的生物标志物仍然有限。本研究探讨非线性动力学能否为DIP提供具有生理学可解释性的标志物，并阐明这些异常如何随元音和锥体外系症状而变化。
方法： 对82例DIP患者和30名健康对照者的五个元音（/a/、/i/、/u/、/e/、/o/）持续发声进行分析。整合了希尔伯特-黄变换（HHT）特征以及样本熵、关联维数、最大李雅普诺夫指数和递归量化分析（RQA）。检验了其与药物诱发性锥体外系症状量表（DIEPSS）各领域的关联。使用外部特发性帕金森病（IPD）数据集和机器学习作为二次分析，以评估诊断效用。
结果： HHT衍生特征显示出元音依赖性，而非线性指标则揭示DIP中存在更广泛的异常。样本熵和关联维数在圆唇后元音/u/和/o/中升高最为显著。RQA确定性在所有元音中均显著降低，递归率和层流性在多个元音中降低，表明发声的递归性受损。在DIP队列内，运动障碍与声学异常的关联最强，而包括步态、运动迟缓、强直、震颤、运动障碍和总体严重程度在内的更广泛运动领域则刻画了患者与对照之间的差异。使用随机森林的二次分析实现了76.0%（CI 70.3-81.3%）的三分类。
结论： DIP与可测量的非线性发声动力学紊乱相关，其特征为元音敏感的HHT和熵特征以及RQA确定性相对一致的降低。这些可解释的声学模式可能为表征锥体外系运动功能障碍提供一种无创手段。
英文摘要
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.