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

Adrianna Shembel, Jeon Lee, Joshua R. Sacher, Aaron M. Johnson

Research output: Contribution to journalArticlepeer-review

Abstract

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

Original languageEnglish (US)
JournalJournal of Voice
DOIs
StateAccepted/In press - 2021

Keywords

  • Muscle tension dysphonia, instrumental acoustic, aerodynamic, voice diagnostics, random forest, regression, affinity clustering

ASJC Scopus subject areas

  • Otorhinolaryngology
  • LPN and LVN
  • Speech and Hearing

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