A Detailed Motion Analysis of the Angular Velocity Between the Vocal Folds During Throat Clearing Using High-speed Digital Imaging

Iwahashi, Toru; Ogawa, Makoto; Hosokawa, Kiyohito; Kato, Chieri; Inohara, Hidenori

doi:10.1016/j.jvoice.2015.11.004

Cited by 12 publications

(14 citation statements)

References 25 publications

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“…The present findings are consistent with previous work by Iwahashi et al (2016), in which the closing gesture can be seen decelerating prior to VF impact at phonation onset. Similarly, in the present data, monotonically decreasing angle trajectories during adduction were consistently observed.…”

Section: Modeling the Behavior Of Adduction Trajectories As Sigmoidal Functionssupporting

confidence: 93%

“…The authors focused on examining motion of the laryngeal structures during sustained phonation and throat clears. Although the adduction trajectories showed polynomial-like curves rather than sigmoidal curves during throat clears, the authors suggested that adduction trajectories were generally well fit by a sigmoidal model for sustained phonation (Iwahashi et al, 2016). However, these findings were based on qualitative impressions from sustained phonation.…”

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confidence: 94%

“…Recently, the incorporation of new imaging technology in medical instrumentation has led to improvements in time resolution of laryngoscopy procedures (Mehta & Hillman, 2012). Laryngeal examination via high-speed videoendoscopy (HSV) provides higher temporal resolution (much faster than 30 fps), which allows for accurate estimation of both vibratory features (Deliyski, Powell, Zacharias, Gerlach, & de Alarcon, 2015;Patel, Dubrovskiy, & Döllinger, 2014) and VF kinematic trajectories (Freeman, Woo, Saxman, & Murry, 2012;Iwahashi, Ogawa, Hosokawa, Kato, & Inohara, 2016). Several approaches using HSV have been developed to examine characteristics of steady-state phonation and associated vocal onsets and offsets (e.g., Aghdam et al, 2017;Braunschweig, Flaschka, Schelhorn-Neise, & Döllinger, 2008;Döllinger, Dubrovskiy, & Patel, 2012;Freeman et al, 2012;Guzman et al, 2017;Ikuma, Kunduk, Fink, & McWhorter, 2016;Iwahashi et al, 2016;Kunduk, Döllinger, McWhorter, & Lohscheller, 2010;Kunduk, Vansant, Ikuma, & McWhorter, 2017;Kunduk, Yan, McWhorter, & Bless, 2006;Mehta, Deliyski, Quatieri, & Hillman, 2011;Patel et al, 2014;Patel, Forrest, & Hedges, 2017;Patel, Unnikrishnan, & Donohue, 2016;Patel, Walker, & Sivasankar, 2016;Watanabe, Kaneko, Sakaguchi, & Takahashi, 2016;Woo, 2017;Yamauchi et al, 2016;Zacharias, Deliyski, & Gerlach, 2018); yet, to date, there are few studies characterizing VF kinematics of laryngeal posturing during connected speech.…”

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confidence: 99%

“…Indeed, researchers using HSV to investigate steady-state voice production have shown that a frame rate of 4,000 fps is required for an accurate assessment of VF vibratory features (Deliyski et al, 2015). Recently, a study by Iwahashi et al (2016) used an HSV system recording at 4,000 fps to investigate whether the parametric fit approach used in previous studies was a valid means of analyzing laryngeal kinematics. The authors focused on examining motion of the laryngeal structures during sustained phonation and throat clears.…”

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confidence: 99%

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Adductory Vocal Fold Kinematic Trajectories During Conventional Versus High-Speed Videoendoscopy

Díaz-Cádiz

McKenna

Vojtech

et al. 2019

J Speech Lang Hear Res

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Objective Prephonatory vocal fold angle trajectories may supply useful information about the laryngeal system but were examined in previous studies using sigmoidal curves fit to data collected at 30 frames per second (fps). Here, high-speed videoendoscopy (HSV) was used to investigate the impacts of video frame rate and sigmoidal fitting strategy on vocal fold adductory patterns for voicing onsets. Method Twenty-five participants with healthy voices performed /ifi/ sequences under flexible nasendoscopy at 1,000 fps. Glottic angles were extracted during adduction for voicing onset; resulting vocal fold trajectories (i.e., changes in glottic angle over time) were down-sampled to simulate different frame rate conditions (30–1,000 fps). Vocal fold adduction data were fit with asymmetric sigmoids using 5 fitting strategies with varying parameter restrictions. Adduction trajectories and maximum adduction velocities were compared between the fits and the actual HSV data. Adduction trajectory errors between HSV data and fits were evaluated using root-mean-square error and maximum angular velocity error. Results Simulated data were generally well fit by sigmoid models; however, when compared to the actual 1,000-fps data, sigmoid fits were found to overestimate maximum angle velocities. Errors decreased as frame rate increased, reaching a plateau by 120 fps. Conclusion In healthy adults, vocal fold kinematic behavior during adduction is generally sigmoidal, although such fits can produce substantial errors when data are acquired at frame rates lower than 120 fps.

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Section: Modeling the Behavior Of Adduction Trajectories As Sigmoidal Functionssupporting

confidence: 93%

mentioning

confidence: 94%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Adductory Vocal Fold Kinematic Trajectories During Conventional Versus High-Speed Videoendoscopy

Díaz-Cádiz

McKenna

Vojtech

et al. 2019

J Speech Lang Hear Res

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“…Manual marking of videolaryngoscopy frames to quantify vocal fold dynamics among healthy adults was employed by Dailey et al 10 during normal inspiration and phonation, by Britton et al 11 during cough, and by Kuna et al 12 during forced vital capacity maneuvers. Use of various traditional image processing techniques for automated tracking of vocal fold dynamics from videolaryngoscopy was reported by Olin et al 13 in the setting of exercise induced stridor, by Iwahashi et al 14 during throat clearing, and by Mendez et a.l 15 in patients with VFP. These reports, however, did not report comparison of algorithm estimations against expert markings.…”

Section: Introductionmentioning

confidence: 99%

An Open‐Source Computer Vision Tool for Automated Vocal Fold Tracking From Videoendoscopy

2020

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Objectives Contemporary clinical assessment of vocal fold adduction and abduction is qualitative and subjective. Herein is described a novel computer vision tool for automated quantitative tracking of vocal fold motion from videolaryngoscopy. The potential of this software as a diagnostic aid in unilateral vocal fold paralysis is demonstrated. Study Design Case‐control. Methods A deep‐learning algorithm was trained for vocal fold localization from videoendoscopy for automated frame‐wise estimation of glottic opening angles. Algorithm accuracy was compared against manual expert markings. Maximum glottic opening angles between adults with normal movements (N = 20) and those with unilateral vocal fold paralysis (N = 20) were characterized. Results Algorithm angle estimations demonstrated a correlation coefficient of 0.97 (P < .001) and mean absolute difference of 3.72° (standard deviation [SD], 3.49°) in comparison to manual expert markings. In comparison to those with normal movements, patients with unilateral vocal fold paralysis demonstrated significantly lower maximal glottic opening angles (mean 68.75° ± 11.82° vs. 49.44° ± 10.42°; difference, 19.31°; 95% confidence interval [CI] [12.17°–26.44°]; P < .001). Maximum opening angle less than 58.65° predicted unilateral vocal fold paralysis with a sensitivity of 0.85 and specificity of 0.85, with an area under the receiver operating characteristic curve of 0.888 (95% CI [0.784–0.991]; P < .001). Conclusion A user‐friendly software tool for automated quantification of vocal fold movements from previously recorded videolaryngoscopy examinations is presented, termed automated glottic action tracking by artificial intelligence (AGATI). This tool may prove useful for diagnosis and outcomes tracking of vocal fold movement disorders. Level of Evidence IV Laryngoscope, 131:E219–E225, 2021

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Computational Analysis of the Droplet‐Stimulated Laryngeal Adductor Reflex in High‐Speed Sequences

et al. 2022

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Objectives/Hypothesis The laryngeal adductor reflex (LAR) is an important protective mechanism of the airways. Its physiology is still not completely understood. The available methods for LAR evaluation offer limited reproducibility and/or rely on subjective interpretation. A new approach, termed Microdroplet Impulse Testing of the LAR (MIT‐LAR), was recently introduced. Here, the LAR is elicited by a droplet and a laryngoscopic high‐speed recording is acquired simultaneously. In the present work, image‐processing algorithms for autonomous MIT‐LAR sequence analysis were developed. This allowed the automated approximation of kinematic LAR parameters in humans. Study Design Development and testing of computational methods. Methods Computational image processing enabled the autonomous estimation of the glottal area, the glottal angle, and the vocal fold edge distance in MIT‐LAR sequences. A suitable analytical representation of these glottal parameters allowed the extraction of seven relevant LAR parameters. The obtained values were compared to the literature. Results A generalized logistic function showed the highest average goodness of fit among four different analytical approaches for each of the glottal parameters. Autonomous sequence analysis yielded bilateral LAR response latencies of (229 ± 116) ms and (182 ± 60) ms for cases of complete and incomplete glottal closure, respectively. The initial/average/maximum angular vocal fold adduction velocity was estimated at (157 ± 115) °s−1/(891 ± 516) °s−1/(929 ± 583) °s−1 and (88 ± 53) °s−1/(421 ± 221) °s−1/(520 ± 238) °s−1 for complete and incomplete glottal closure, respectively. Conclusion The automated extraction of LAR parameters from laryngoscopic high‐speed sequences can potentially increase the objectiveness of optical LAR characterization and reduce the associated workload. The proposed methods may thus be helpful for future research on this vital reflex. Level of Evidence NA Laryngoscope, 132:2412–2419, 2022

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A Detailed Motion Analysis of the Angular Velocity Between the Vocal Folds During Throat Clearing Using High-speed Digital Imaging

Cited by 12 publications

References 25 publications

Adductory Vocal Fold Kinematic Trajectories During Conventional Versus High-Speed Videoendoscopy

Adductory Vocal Fold Kinematic Trajectories During Conventional Versus High-Speed Videoendoscopy

An Open‐Source Computer Vision Tool for Automated Vocal Fold Tracking From Videoendoscopy

Computational Analysis of the Droplet‐Stimulated Laryngeal Adductor Reflex in High‐Speed Sequences

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