Laryngeal muscular control of vocal fold posturing: Numerical modeling and experimental validation

Yin, Jun; Zhang, Zhaoyan

doi:10.1121/1.4962375

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…Human voice is produced by the flow of air from the lungs through the larynx, which causes the vocal fold vibrations, generating a pulsating airstream 39. The process is controlled by the laryngeal muscle activation40 but involves the entire respiratory system to provide the air pressure necessary for phonation. Decreased pulmonary function in COVID-19 patients can cause reduced glottal airflow that is essential for normal voice production 41.…”

Section: Discussionmentioning

confidence: 99%

Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study

et al. 2022

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ObjectiveTo develop a vocal biomarker for fatigue monitoring in people with COVID-19.DesignProspective cohort study.SettingPredi-COVID data between May 2020 and May 2021.ParticipantsA total of 1772 voice recordings were used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone’s operating system (Android/iOS). The recordings were collected from 296 participants tracked for 2 weeks following SARS-CoV-2 infection.Primary and secondary outcome measuresFour machine learning algorithms (logistic regression, k-nearest neighbours, support vector machine and soft voting classifier) were used to train and derive the fatigue vocal biomarker. The models were evaluated based on the following metrics: area under the curve (AUC), accuracy, F1-score, precision and recall. The Brier score was also used to evaluate the models’ calibrations.ResultsThe final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (p<0.001). We developed four models for Android female, Android male, iOS female and iOS male users with a weighted AUC of 86%, 82%, 79%, 85% and a mean Brier Score of 0.15, 0.12, 0.17, 0.12, respectively. The vocal biomarker derived from the prediction models successfully discriminated COVID-19 participants with and without fatigue.ConclusionsThis study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID.Trial registration numberNCT04380987.

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Section: Discussionmentioning

confidence: 99%

Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Human voice is produced by the flow of air from the lungs through the larynx, which causes the vocal fold vibrations, generating a pulsating air stream 39 . The process is controlled by the laryngeal muscle activation 40 but involves the entire respiratory system to provide the air pressure necessary for phonation. Decreased pulmonary function in COVID-19 patients can cause reduced glottal airflow that is essential for normal voice production 41 .…”

Section: Discussionmentioning

confidence: 99%

Development of a vocal biomarker for fatigue monitoring in people with COVID-19

Elbéji

Zhang

Higa

et al. 2022

Preprint

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ObjectiveTo develop a vocal biomarker for fatigue monitoring in people with COVID-19.DesignProspective cohort study.SettingPredi-COVID data between May 2020 and May 2021.ParticipantsA total of 1772 voice recordings was used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone’s operating system (Android/iOS). The recordings were collected from 296 participants tracked for two weeks following SARS-CoV-2 infection.primary and secondary outcome measuresFour machine learning algorithms (Logistic regression, k-nearest neighbors, support vector machine, and soft voting classifier) were used to train and derive the fatigue vocal biomarker. A t-test was used to evaluate the distribution of the vocal biomarker between the two classes (Fatigue and No fatigue).ResultsThe final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (P<.001). We developed four models for Android female, Android male, iOS female, and iOS male users with a weighted AUC of 79%, 85%, 86%, 82%, and a mean Brier Score of 0.15, 0.12, 0.17, 0.12, respectively. The vocal biomarker derived from the prediction models successfully discriminated COVID-19 participants with and without fatigue (t-test P<.001).ConclusionsThis study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID.

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“…For example, Yin and Zhang [15] constrained relative motion along the medial-lateral direction but allowed a relative anterior-posterior sliding motion in order to model vocal fold elongation due to cricothyroid muscle activation. In Yin and Zhang [14,16], the paraglottic space was modeled as a spring foundation attached to the lateral surface of the vocal folds in order to model vocal fold adduction.…”

Section: Introductionmentioning

confidence: 99%

Laryngeal muscular control of vocal fold posturing: Numerical modeling and experimental validation

Cited by 12 publications

References 22 publications

Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study

Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study

Development of a vocal biomarker for fatigue monitoring in people with COVID-19

Impact of the Paraglottic Space on Voice Production in an MRI-Based Vocal Fold Model

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