Characterization of Primary Muscle Tension Dysphonia Using Acoustic and Aerodynamic Voice Metrics.

Autor: Shembel AC; Department of Speech, Language, and Hearing, University of Texas at Dallas, Dallas, Texas; Department of Otolaryngology-Head and Neck Surgery, University of Texas at Southwestern Medical Center, Dallas, Texas; Department of Otolaryngology-Head and Neck Surgery, New York University School of Medicine, New York, New York. Electronic address: adrianna.shembel@utdallas.edu., Lee J; Lyda Hill Department of Bioinformatics, University of Texas at Southwestern, Dallas, Texas., Sacher JR; Center for the Development of Therapeutics, Broad Institute, Cambridge, Massachusetts., Johnson AM; Department of Otolaryngology-Head and Neck Surgery, New York University School of Medicine, New York, New York.
Jazyk: angličtina
Zdroj: Journal of voice : official journal of the Voice Foundation [J Voice] 2023 Nov; Vol. 37 (6), pp. 897-906. Date of Electronic Publication: 2021 Jul 17.
DOI: 10.1016/j.jvoice.2021.05.019
Abstrakt: Objectives/hypothesis: 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.
Study Design: Retrospective multiparametric METHODS: Random forest modeling, independent t-tests, logistic regression, and affinity propagation clustering were implemented on a retrospective dataset of 15 acoustic and aerodynamic metrics.
Results: 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.
Conclusion: 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.
Level of Evidence: II.
Competing Interests: CONFLICT OF INTEREST None of the authors have a conflict of interest to declare.
(Copyright © 2021 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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