The Nyquist plot provides a graphical representation of the glottal cycles as elliptical trajectories in a 2D plane. This study proposes a methodology to parameterize the Nyquist plot with application to support the quantitative analysis of voice disorders.
We considered high-speed videoendoscopy recordings of 33 functional dysphonia (FD) patients and 33 normophonic controls (NC). Quantitative analysis was performed by computing four shape-based parameters from the Nyquist plot: Variability, Size (Perimeter and Area), and Consistency. Additionally, we performed automatic classification using a linear support vector machine and feature importance analysis by combining the proposed features with state-of-the-art glottal area waveform (GAW) parameters.
We found that the inter-cycle variability was significantly higher in FD patients compared to NC. We achieved a classification accuracy of 83 when the top 30 most important features were used. Furthermore, the proposed Nyquist plot features were ranked in the top 12 most important features.
The Nyquist plot provides complementary information for subjective and objective assessment of voice disorders. On the one hand, with visual inspection it is possible to observe intra- and inter-glottal cycle irregularities during sustained phonation. On the other hand, shaped-based parameters allow quantifying such irregularities and provide complementary information to state-of-the-art GAW parameters.
To read this article in full you will need to make a payment
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:Subscribe to Journal of Voice
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
- Multi-dimensional investigation of the clinical effectiveness and prognostic factors of voice therapy for benign voice disorders.J Formosan Med Assoc. 2022; 121: 329-334
- Voice disorders in the elderly: A national database study.Laryngoscope. 2016; 126: 421-428
- Voice changes in Parkinson’s disease: what are they telling us?.J Clin Neurosci. 2020; 72: 1-7
- Utility of laryngeal high-speed videoendoscopy in clinical voice assessment.J Voice. 2018; 32: 216-220
- Characterizing vibratory kinematics in children and adults with high-speed digital imaging.J Speech Lang Hearing Res. 2014; 57: 674-686
- Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings.Scient Rep. 2020; 10: 10517
- Segmentation of glottal images from high-speed videoendoscopy optimized by synchronous acoustic recordings.Sensors. 2022; 22: 1751
- Impact of phonatory frequency and intensity on glottal area waveform measurements derived from high-speed videoendoscopy.J Acoust Soc Am. 2018; 143
- Laryngeal high-speed videoendoscopy normal vocal fold symmetry and phase characteristics.in: Kendall K.A. Leonard R.J. Laryngeal Evaluation. Georg Thieme Verlag KG, Stuttgart2010: 135-139
- Investigating acoustic correlates of human vocal fold vibratory phase asymmetry through modeling and laryngeal high-speed videoendoscopy.J Acoust Soc Am. 2011; 130: 3999-4009
- Clinical relevance of endoscopic three-dimensional imaging for quantitative assessment of phonation.Laryngoscope. 2018; 128: 2367-2374
- Effects of vocal fold nodules on glottal cycle measurements derived from high-speed videoendoscopy in children.PLOS One. 2016; 11: e0154586
- Influence of spatial camera resolution in high-speed videoendoscopy on laryngeal parameters.PLOS One. 2019; 14: e0215168
- Analysis of vocal-fold vibrations from high-speed laryngeal images using a Hilbert transform-based methodology.J Voice. 2005; 19: 161-175
- Vocal fold vibratory characteristics in normal female speakers from high-speed digital imaging.J Voice. 2012; 26: 239-253
- Description of the features and vibratory behaviors of the nyquist plot analyzed from laryngeal high-speed videoendoscopy images.J Voice. 2022; 36: 582.e11-582.e22
- Effects of using laryngeal high-speed videoendoscopy images visualizing partial views of the glottis on measurement outcomes.J Voice. 2022; 36: 106-112
- Assessment of Clinical Voice Parameters and Parameter Reduction Using Supervised Learning Approaches.Shaker Verlag, 2020
- A deep learning enhanced novel software tool for laryngeal dynamics analysis.J Speech Lang Hearing Res. 2021; 64: 1889-1903
- Clinical applications of Nyquist plot and time-frequency analysis of HSDP records of selected dysphonias.ePhonoscope. 2016; : 99-106
- Applications of artificial intelligence to office laryngoscopy: a scoping review.Laryngoscope. 2022; 132: 1993-2016
- A basic protocol for functional assessment of voice pathology, especially for investigating the efficacy of (phonosurgical) treatments and evaluating new assessment techniques.European Archives of Otorhinolaryngology. 2001; 258: 77-82
- Die auditive Beurteilung heiserer Stimmen nach dem RBH-System.Sprache Stimme Gehör. 1994; 18: 130-133
- Handbook of the International Phonetic Association: A guide to the use of the International Phonetic Alphabet.Cambridge University Press, 1999
- Measurement of mucosal wave propagation and vertical phase difference in vocal fold vibration.Annals of Otology, Rhinology & Laryngology. 1993; 102: 58-63
- Laryngeal biomechanics: an overview of mucosal wave mechanics.J Voice. 1993; 7: 123-128
- Intralimb coordination as a sensitive indicator of motor-control impairment after spinal cord injury.Front Hum Neurosci. 2014; 8: 00148
- A new gait parameterization technique by means of cyclogram moments: Application to human slope walking.Gait Posture. 1998; 8: 15-36
- Representativity of 2D shape parameters for mineral particles in quantitative petrography.Minerals. 2019; 9: 768
- A method of assigning numerical and percentage values to the degree of roundness of sand grains.J Paleontol. 1927; 1: 179-183
- Support-vector networks.Mach Learn. 1995; 20: 273-297
- Pingouin: statistics in python.J Open Source Software. 2018; 3
- On combining biclustering mining and AdaBoost for breast tumor classification.IEEE Trans Knowl Data Eng. 2019; 32: 728-738
- Random forest-based prediction of stroke outcome.Scient Rep. 2021; 11: 10071
- Parkinson’s disease and aging: analysis of their effect in phonation and articulation of speech.Cognit Comput. 2017; 9: 731-748
- Assessment of the variability of vocal fold dynamics within and between recordings with high-speed imaging and by phonovibrogram.Laryngoscope. 2010; 120: 981-987
Published online: February 09, 2023
Accepted: January 12, 2023
Publication stageIn Press Corrected Proof
© 2023 The Voice Foundation. Published by Elsevier Inc. All rights reserved.