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Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update
Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, referred to as vocal hyperfunction. The clinical management of hyperfunctional voice disorders would be greatly enhanced by the ability to monitor and...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607864/ https://www.ncbi.nlm.nih.gov/pubmed/26528472 http://dx.doi.org/10.3389/fbioe.2015.00155 |
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author | Mehta, Daryush D. Van Stan, Jarrad H. Zañartu, Matías Ghassemi, Marzyeh Guttag, John V. Espinoza, Víctor M. Cortés, Juan P. Cheyne, Harold A. Hillman, Robert E. |
author_facet | Mehta, Daryush D. Van Stan, Jarrad H. Zañartu, Matías Ghassemi, Marzyeh Guttag, John V. Espinoza, Víctor M. Cortés, Juan P. Cheyne, Harold A. Hillman, Robert E. |
author_sort | Mehta, Daryush D. |
collection | PubMed |
description | Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, referred to as vocal hyperfunction. The clinical management of hyperfunctional voice disorders would be greatly enhanced by the ability to monitor and quantify detrimental vocal behaviors during an individual’s activities of daily life. This paper provides an update on ongoing work that uses a miniature accelerometer on the neck surface below the larynx to collect a large set of ambulatory data on patients with hyperfunctional voice disorders (before and after treatment) and matched-control subjects. Three types of analysis approaches are being employed in an effort to identify the best set of measures for differentiating among hyperfunctional and normal patterns of vocal behavior: (1) ambulatory measures of voice use that include vocal dose and voice quality correlates, (2) aerodynamic measures based on glottal airflow estimates extracted from the accelerometer signal using subject-specific vocal system models, and (3) classification based on machine learning and pattern recognition approaches that have been used successfully in analyzing long-term recordings of other physiological signals. Preliminary results demonstrate the potential for ambulatory voice monitoring to improve the diagnosis and treatment of common hyperfunctional voice disorders. |
format | Online Article Text |
id | pubmed-4607864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-46078642015-11-02 Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update Mehta, Daryush D. Van Stan, Jarrad H. Zañartu, Matías Ghassemi, Marzyeh Guttag, John V. Espinoza, Víctor M. Cortés, Juan P. Cheyne, Harold A. Hillman, Robert E. Front Bioeng Biotechnol Bioengineering and Biotechnology Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, referred to as vocal hyperfunction. The clinical management of hyperfunctional voice disorders would be greatly enhanced by the ability to monitor and quantify detrimental vocal behaviors during an individual’s activities of daily life. This paper provides an update on ongoing work that uses a miniature accelerometer on the neck surface below the larynx to collect a large set of ambulatory data on patients with hyperfunctional voice disorders (before and after treatment) and matched-control subjects. Three types of analysis approaches are being employed in an effort to identify the best set of measures for differentiating among hyperfunctional and normal patterns of vocal behavior: (1) ambulatory measures of voice use that include vocal dose and voice quality correlates, (2) aerodynamic measures based on glottal airflow estimates extracted from the accelerometer signal using subject-specific vocal system models, and (3) classification based on machine learning and pattern recognition approaches that have been used successfully in analyzing long-term recordings of other physiological signals. Preliminary results demonstrate the potential for ambulatory voice monitoring to improve the diagnosis and treatment of common hyperfunctional voice disorders. Frontiers Media S.A. 2015-10-16 /pmc/articles/PMC4607864/ /pubmed/26528472 http://dx.doi.org/10.3389/fbioe.2015.00155 Text en Copyright © 2015 Mehta, Van Stan, Zañartu, Ghassemi, Guttag, Espinoza, Cortés, Cheyne and Hillman. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Mehta, Daryush D. Van Stan, Jarrad H. Zañartu, Matías Ghassemi, Marzyeh Guttag, John V. Espinoza, Víctor M. Cortés, Juan P. Cheyne, Harold A. Hillman, Robert E. Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update |
title | Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update |
title_full | Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update |
title_fullStr | Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update |
title_full_unstemmed | Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update |
title_short | Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update |
title_sort | using ambulatory voice monitoring to investigate common voice disorders: research update |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607864/ https://www.ncbi.nlm.nih.gov/pubmed/26528472 http://dx.doi.org/10.3389/fbioe.2015.00155 |
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