Characterization of Primary Muscle Tension Dysphonia Using Acoustic and Aerodynamic Voice Metrics.
Characterization of Primary Muscle Tension Dysphonia Using Acoustic and Aerodynamic Voice Metrics.
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DOI：
10.1016/j.jvoice.2021.05.019
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发表时间：
2023-11
期刊：
影响因子：
2.2
通讯作者：
Johnson, Aaron M.
中科院分区：
文献类型：
--
作者：
Shembel, Adrianna C.;Lee, Jeon;Sacher, Joshua R.;Johnson, Aaron M.
关键词：
本研究的目的是（1）确定美国言语-语言-听力协会（阿莎）推荐的15个标准声学和空气动力学语音指标的最佳聚类，以改善原发性肌肉紧张性发声障碍（pMTD）患者的特征，（2）确定这15个指标的组合，可以区分pMTD与其他类型的语音障碍。回顾性多参数随机森林建模，独立的t检验，逻辑回归，亲和传播聚类进行了15个声学和空气动力学指标的回顾性数据集。在纽约大学（NYU）语音中心两年多的时间里，10%的患者符合pMTD的研究标准（983例患者中的92例），其中65例pMTD患者和701例非pMTD患者在所有15个声学和空气动力学语音指标方面都有完整的数据。PCA图和亲和传播聚类表明，这两组之间的这些参数有很大的重叠。根据随机森林模型的重要性水平，排名最高的参数-（1）发声期间的平均气流（L/sec），（2）发声期间的平均SPL（dB），（3）平均峰值气压（cmH 2 O），（4）最高F0（Hz），（5）CPP平均元音（dB）-仅占方差的65%。T检验显示这些参数中的三个-（1）CPP平均元音（dB），（2）最高F0（Hz）和（3）平均峰值气压（cmH 2 O）-具有统计学显著性;然而，每个参数的log 2倍变化极小。对15个声学和空气动力学语音指标的计算模型和多变量统计测试无法充分表征pMTD并确定两组（pMTD和非pMTD）之间的差异。这些指标的进一步验证是需要与语音启发任务，目标生理挑战的声乐系统从基线声乐声学和空气动力学输出。未来的工作还应该更加关注pMTD患者的发声子系统中生理相关指标（例如，神经肌肉过程，喉呼吸运动学）的验证，而不是传统的发声输出指标（例如，声学，空气动力学）。II
The objectives of this study were to (1) identify optimal clusters of 15 standard acoustic and aerodynamic voice metrics recommended by the American Speech-Language-Hearing Association (ASHA) to improve characterization of patients with primary muscle tension dysphonia (pMTD) and (2) identify combinations of these 15 metrics that could differentiate pMTD from other types of voice disorders. Retrospective multiparametric Random forest modeling, independent t-tests, logistic regression, and affinity propagation clustering were implemented on a retrospective dataset of 15 acoustic and aerodynamic metrics. Ten percent of patients seen at the New York University (NYU) Voice Center over two years met the study criteria for pMTD (92 out of 983 patients), with 65 patients with pMTD and 701 of non-pMTD patients with complete data across all 15 acoustic and aerodynamic voice metrics. PCA plots and affinity propagation clustering demonstrated substantial overlap between the two groups on these parameters. The highest ranked parameters by level of importance with random forest models—(1) mean airflow during voicing (L/sec), (2) mean SPL during voicing (dB), (3) mean peak air pressure (cmH2O), (4) highest F0 (Hz), and (5) CPP mean vowel (dB)—accounted for only 65% of variance. T-tests showed three of these parameters—(1) CPP mean vowel (dB), (2) highest F0 (Hz), and (3) mean peak air pressure (cmH2O)—were statistically significant; however, the log2-fold change for each parameter was minimal. Computational models and multivariate statistical testing on 15 acoustic and aerodynamic voice metrics were unable to adequately characterize pMTD and determine differences between the two groups (pMTD and non-pMTD). Further validation of these metrics is needed with voice elicitation tasks that target physiological challenges to the vocal system from baseline vocal acoustic and aerodynamic ouput. Future work should also place greater focus on validating metrics of physiological correlates (eg, neuromuscular processes, laryngeal-respiratory kinematics) across the vocal subsystems over traditional vocal output measures (eg, acoustics, aerodynamics) for patients with pMTD. II
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作者：
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