原发性肌肉紧张性发声障碍的声学和空气动力集群。

IF 2.2
Sarah Rose Bellavance, Aaron M Johnson
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引用次数: 0

摘要

目的:原发性肌张力性发声障碍(pMTD)是一种声带功能亢进的形式,声带没有预先存在的组织损伤。目前还没有已知的经pMTD的结构或神经原因,也很少有明显的、确凿的证据来可靠地准确诊断个体。此外,在声音评估期间采取的声学和空气动力学测量在该人群中差异很大。本研究的目的是根据声学和空气动力学测量在pMTD患者样本中找到亚组。我们使用计算方法来阐明过去主要是观察到的东西。方法:对纽约大学朗格尼语音和吞咽中心于2021年1月1日至2023年10月1日期间就诊的72例pMTD患者样本进行回顾性图表回顾,收集感兴趣的变量。探索性因子分析是为了在数据中找到更简单的结构。利用每位患者的因子得分,进行k-means聚类分析。结果:探索性因子分析将患者的变量分组,得到三个主轴。这三个主轴分别由非周期、基频和气动测量组成。这些主轴解释了总方差的44.7%。在三个主轴上确定了四组患者。这些特征表现为:(a)声音中大量的非周期性,(b)较低的基频值,(c)较高的基频值,以及(d)高的气动值。结论:在目前的研究中确定的聚类是可靠的和适度分离的。此外,这些集群与先前在相关工作中确定的子组保持一致。这里提出的分析为新的pMTD样本的额外聚类分析以及未来建立pMTD亚型分类的工作奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Acoustic and Aerodynamic Clusters Within Primary Muscle Tension Dysphonia.

Purpose: Primary muscle tension dysphonia (pMTD) is a form of vocal hyperfunction with no preexisting tissue trauma to the vocal folds. There are no known structural or neurological causes of pMTD, and there is rarely obvious, confirmatory evidence to reliably diagnose individuals accurately. Furthermore, acoustic and aerodynamic measurements taken during voice assessments vary widely within this population. The purpose of this study was to find subgroups within a sample of pMTD patients based on acoustic and aerodynamic measurements. We use a computational approach to elucidate what has largely been observational in the past.

Method: A retrospective chart review was conducted to collect variables of interest for a sample of 72 pMTD patients seen at the NYU Langone Voice and Swallowing Center from January 1, 2021, to October 1, 2023. An exploratory factor analysis was conducted to find simpler structures in the data. Using factor scores from each patient, a k-means clustering analysis was conducted.

Results: The exploratory factor analysis grouped together variables across patients, which resulted in three principal axes. These three principal axes separately consisted of aperiodicity, fundamental frequency, and aerodynamic measurements. These principal axes explained 44.7% of the total variance. Four clusters of patients were identified across the three principal axes. These were characterized by (a) a high amount of aperiodicity in the voice, (b) lower fundamental frequency values, (c) higher fundamental frequency values, and (d) high aerodynamic values.

Conclusions: The clusters identified in the current study are reliable and moderately separated. Furthermore, these clusters align with previously identified subgroups in related work. The analysis presented here lays the groundwork for additional clustering analyses with new pMTD samples, as well as future work establishing subtype classifications of pMTD.

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