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Direct numerical simulations and flow-pressure acoustic analyses of flapping-uvula-induced flow evolutions within normal and constricted pharynx

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Abstract

Snoring and obstructive sleep apnea (OSA) are often associated with uvula vibrations and pharynx constrictions. However, successful treatment of snoring or accurate diagnosis of OSA has been proven challenging. This study aimed to identify acoustic indexes that were sensitive to underlying airway structural or kinematic variations. Six physiologically realistic models were developed that consisted of three pharynx constriction levels (M1-3) and two uvula-flapping kinematics (K1-2). Direct numerical simulations (DNS) were performed to resolve spatial and temporal flow dynamics, and an immersed boundary method was used to approximate the uvula vibrations. Time-varying acoustic pressures at six points in the pharynx were analyzed using different algorithms in frequency- or frequency–time domains. Signature flow structures formed near the uvula for different uvula motions and in the pharynx for different pharyngeal constriction levels. The fast Fourier transform showed that the acoustic energy was mainly distributed in four peaks (flapping frequency and three harmonics) with descending magnitudes. Their amplitudes and distribution patterns differed among the six models but were not substantial. The continuous wavelet transforms showed clearly separated acoustic cycles (in both frequency and time) in the uvula-induced flows and revealed a cascading bifurcation pattern in the input–output semblance map. Specifically, the multifractal spectrum was sensitive to uvula flapping kinematics but not pharynx constrictions. By contrast, the input–output cross-correlation and Hilbert phase space showed high sensitivity to pharynx constrictions but low sensitivity to uvula kinematics. The frequency–time analyses of DNS-predicted pressures offered insight into the acoustics signals that were not apparent in original signals and could be used individually or in combination in diagnosis or treatment planning for snoring/OSA patients.

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The DNS simulation results and MATLAB analysis codes are available upon request.

References

  1. Huang, Y., White, D.P., Malhotra, A.: Use of computational modeling to predict responses to upper airway surgery in obstructive sleep apnea. Laryngoscope 117, 648–653 (2007)

    Article  Google Scholar 

  2. Aragon, S.B.: Surgical management for snoring and sleep apnea. Dent. Clin. North Am. 45, 867–879 (2001)

    Article  Google Scholar 

  3. Key, A.P.F., Molfese, D.L., O’Brien, L., Gozal, D.: Sleep-disordered breathing affects auditory processing in 5-7-year-old children: evidence from brain recordings. Dev. Neuropsychol. 34, 615–628 (2009)

    Article  Google Scholar 

  4. Bonsignore, M.R., Baiamonte, P., Mazzuca, E., Castrogiovanni, A., Marrone, O.: Obstructive sleep apnea and comorbidities: a dangerous liaison. Multidiscip. Respir. Med. 14, 8–8 (2019)

    Article  Google Scholar 

  5. Tietjens, J.R., Claman, D., Kezirian, E.J., De Marco, T., Mirzayan, A., et al.: Obstructive sleep apnea in cardiovascular disease: a review of the literature and proposed multidisciplinary clinical management strategy. J. Am. Heart Assoc. 8, e010440 (2019)

    Article  Google Scholar 

  6. Touboul, P.J., Grobbee, D.E., den Ruijter, H.: Assessment of subclinical atherosclerosis by carotid intima media thickness: technical issues. Eur. J. Prev. Cardiol. 19, 18–24 (2012)

    Article  Google Scholar 

  7. Dalmasso, F., Prota, R.: Snoring: analysis, measurement, clinical implications and applications. Eur. Respir. J. 9, 146–159 (1996)

    Article  Google Scholar 

  8. Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., et al.: The occurrence of sleep-disordered breathing among middle-aged adults. N. Engl. J. Med. 328, 1230–1235 (1993)

    Article  Google Scholar 

  9. Lindberg, E., Elmasry, A., Gislason, T., Janson, C., Bengtsson, H., et al.: Evolution of sleep apnea syndrome in sleepy snorers: a population-based prospective study. Am. J. Respir. Crit. Care Med. 159, 2024–2027 (1999)

    Article  Google Scholar 

  10. Punjabi, N.M.: The epidemiology of adult obstructive sleep apnea. Proc. Am. Thorac. Soc. 5, 136–143 (2008)

    Article  Google Scholar 

  11. Subramani, Y., Singh, M., Wong, J., Kushida, C.A., Malhotra, A., et al.: Understanding phenotypes of obstructive sleep apnea: applications in anesthesia, srgery, and prioperative mdicine. Anesth. Analg. 124, 179–191 (2017)

    Article  Google Scholar 

  12. Osman, A.M., Carter, S.G., Carberry, J.C., Eckert, D.J.: Obstructive sleep apnea: current perspectives. Nat. Sci. Sleep. 10, 21–34 (2018)

    Article  Google Scholar 

  13. Akhter, S., Abeyratne, U.R., Swarnkar, V., Hukins, C.: Snore sound analysis can detect the presence of obstructive sleep apnea specific to NREM or REM sleep. J. Clin. Sleep Med. 14, 991–1003 (2018)

    Article  Google Scholar 

  14. Ramar, K., Dort, L.C., Katz, S.G., Lettieri, C.J., Harrod, C.G., et al.: Clinical practice guideline for the treatment of obstructive sleep apnea and snoring with oral appliance therapy: an update for 2015. J. Clin. Sleep Med. 11, 773–827 (2015)

    Article  Google Scholar 

  15. Wang, J., Xi, J., Han, P., Wongwiset, N., Pontius, J., et al.: Computational analysis of a flapping uvula on aerodynamics and pharyngeal wall collapsibility in sleep apnea. J. Biomech. 94, 88–98 (2019)

    Article  Google Scholar 

  16. Xi, J., Si, X., Kim, J., Su, G., Dong, H.: Modeling the pharyngeal anatomical effects on breathing resistance and aerodynamically generated sound. Med. Biol. Eng. Comput. 52, 567–577 (2014)

    Article  Google Scholar 

  17. Xi, J., Wang, Z., Talaat, K., Glide-Hurst, C., Dong, H.J.S., et al.: Numerical study of dynamic glottis and tidal breathing on respiratory sounds in a human upper airway model. Sleep Breath. 22, 463–479 (2018)

    Article  Google Scholar 

  18. Xi, J., April Si, X., Dong, H., Zhong, H.: Effects of glottis motion on airflow and energy expenditure in a human upper airway model. Eur. J. Mech. B. Fluids 72, 23–37 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  19. Zinchuk, A., Yaggi, H.K.: Phenotypic subtypes of OSA: a challenge and opportunity for precision medicine. Chest 157, 403–420 (2020)

    Article  Google Scholar 

  20. Naughton, M.T.: Loop gain in apnea: Gaining control or controlling the gain? Am. J. Respir. Crit. Care Med. 181, 103–105 (2010)

    Article  Google Scholar 

  21. Xi, J., Talaat, M., Si, X.A., Dong, H.: Flow dynamics and acoustics from glottal vibrations at different frequencies. Acoustics 4, 1939796 (2022)

    Article  Google Scholar 

  22. Messineo, L., Taranto-Montemurro, L., Azarbarzin, A., Oliveira Marques, M.D., Calianese, N., et al.: Breath-holding as a means to estimate the loop gain contribution to obstructive sleep apnoea. J. Physiol. 596, 4043–4056 (2018)

    Article  Google Scholar 

  23. Gottlieb, D.J., Punjabi, N.M.: Diagnosis and management of obstructive sleep apnea: a review. JAMA 323, 1389–1400 (2020)

    Article  Google Scholar 

  24. Kim, H.-H., Rakibuzzaman, M., Suh, S.-H., Kim, H.-J., Choi, J.-Y., et al.: A study of fluid dynamics parameters for prediction of obstructive sleep apnea. J. Mech. Sci. Tech. 32, 1079–1085 (2018)

    Article  Google Scholar 

  25. Wakayama, T., Suzuki, M., Tanuma, T.: Effect of nasal obstruction on continuous positive airway pressure treatment: computational fluid dynamics analyses. PLoS ONE 11, e0150951 (2016)

    Article  Google Scholar 

  26. Mihaescu, M., Murugappan, S., Gutmark, E., Donnelly, L.F., Kalra, M.: Computational modeling of upper airway before and after adenotonsillectomy for obstructive sleep apnea. Laryngoscope 118, 360–362 (2008)

    Article  Google Scholar 

  27. Sittitavornwong, S., Waite, P., Shih, A., Cheng, G., Koomullil, R., et al.: Computational fluid dynamic analysis of the posterior airway space after maxillomandibular advancement for obstructive sleep apnea syndrome. J. Oral. Maxillofac. Surg. 71, 1397–405 (2013)

    Article  Google Scholar 

  28. Taherian, S., Rahai, H., Lopez, S., Shin, J., Jafari, B.: Evaluation of human obstructive sleep apnea using computational fluid dynamics. Commun. Biol. 2, 423 (2019)

    Article  Google Scholar 

  29. Back, G.W., Nadig, S., Uppal, S., Coatesworth, A.P.: Why do we have a uvula?: literature review and a new theory. Clin. Otolaryngol. Allied Sci. 29, 689–693 (2004)

    Article  Google Scholar 

  30. Xi, J., Wang, J., Si, X.A., Zheng, S., Donepudi, R., et al.: Extracting signature responses from respiratory flows: low-dimensional analyses on Direct Numerical Simulation-predicted wakes of a flapping uvula. Int. J. Numer. Method Biomed. Eng. 36, e3406 (2020)

    Article  MathSciNet  Google Scholar 

  31. Clark, A., Newman, S., Dasovich, N.: Mouth and oropharyngeal deposition of pharmaceutical aerosols. J. Aerosol Med. 11, S116–S120 (1998)

    Article  Google Scholar 

  32. Borojeni, A.A.T., Garcia, G.J.M., Moghaddam, M.G., Frank-Ito, D.O., Kimbell, J.S., et al.: Normative ranges of nasal airflow variables in healthy adults. Int. J. Comput. Assist. Radiol. Surg. 15, 87–98 (2020)

    Article  Google Scholar 

  33. Mittal, R., Dong, H., Bozkurttas, M., Najjar, F.M., Vargas, A., et al.: A versatile sharp interface immersed boundary method for incompressible flows with complex boundaries. J. Comput. Phys. 227, 4825–4852 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  34. Wang, J., Wainwright, D.K., Lindengren, R.E., Lauder, G.V., Dong, H.: Tuna locomotion: a computational hydrodynamic analysis of finlet function. J. R. Soc. Interface 17, 20190590 (2020)

    Article  Google Scholar 

  35. Li, C., Dong, H., Liu, G.: Effects of a dynamic trailing-edge flap on the aerodynamic performance and flow structures in hovering flight. J. Fluid Struct. 58, 49–65 (2015)

    Article  Google Scholar 

  36. Wang, J., Ren, Y., Li, C., Dong, H.: Computational investigation of wing-body interaction and its lift enhancement effect in hummingbird forward flight. Bioinspir. Biomim. 14, 046010 (2019)

    Article  Google Scholar 

  37. Ren, Y., Dong, H., Deng, X., Tobalske, B.: Turning on a dime: asymmetric vortex formation in hummingbird maneuvering flight. Phys. Rev. Fluids. 1, 050511 (2016)

    Article  Google Scholar 

  38. Liu, G., Dong, H., Li, C.: Vortex dynamics and new lift enhancement mechanism of wing-body interaction in insect forward flight. J. Fluid Mech. 795, 634–651 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  39. Wan, H., Dong, H., Gai, K.: Computational investigation of cicada aerodynamics in forward flight. J. R. Soc. Interface 12, 20141116 (2015)

    Article  Google Scholar 

  40. Dong, H., Mittal, R., Najjar, F.M.: Wake topology and hydrodynamic performance of low-aspect-ratio flapping foils. J. Fluid Mech. 566, 309–343 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  41. Hekmati, A., Ricot, D., Druault, P.: About the convergence of POD and EPOD modes computed from CFD simulation. Comput. Fluids 50, 60–71 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  42. Hunt, J., Wray, A., Moin, P.: Eddies, streams, and convergence zones in turbulent flows. Studying Turbulence Using Numerical Simulation Databases, pp. 193–208 (1988)

  43. Grossmann, A., Morlet, J.: Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15, 723–736 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  44. Subbu, A., Ray, A. Space partitioning via Hilbert transform for symbolic time series analysis (2008)

  45. Jubran, B.A., Hamdan, M.N., Shabanneh, N.H., Szepessy, S.: Wavelet and chaos analysis of irregularities of vortex shedding. Mech. Res. Commun. 25, 583–591 (1998)

    Article  MATH  Google Scholar 

  46. Oczeretko, E., Swiatecka, J., Kitlas, A., Laudanski, T., Pierzynski, P.: Visualization of synchronization of the uterine contraction signals: running cross-correlation and wavelet running cross-correlation methods. Med. Eng. Phys. 28, 75–81 (2006)

    Article  Google Scholar 

  47. Elghobashi, S.: Direct numerical simulation of turbulent flows laden with droplets or bubbles. Annu. Rev. Fluid Mech. 51, 217–244 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  48. Xi, J., Si, X.A., Kim, J., Mckee, E., Lin, E.-B.: Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis. PLoS ONE 9, e104682 (2014)

    Article  Google Scholar 

  49. Calderón-Díaz, M., Ulloa-Jiménez, R., Saavedra, C., Salas, R.: Wavelet-based semblance analysis to determine muscle synergy for different handstand postures of Chilean circus athletes. Comput. Methods Biomech. Biomed. Eng. 24, 1053–1063 (2021)

    Article  Google Scholar 

  50. Pickering, D.N., Beardsmore, C.S.: Nasal flow limitation in children. Pediatr. Pulmonol. 27, 32–36 (1999)

    Article  Google Scholar 

  51. Ohki, M., Ogoshi, T., Yuasa, T., Kawano, K., Kawano, M.: Extended observation of the nasal cycle using a portable rhinoflowmeter. J. Otolaryngol. 34, 346–349 (2005)

    Article  Google Scholar 

  52. Xi, J., Kim, J., Si, X.A.: Effects of nostril orientation on airflow dynamics, heat exchange, and particle depositions in human noses. Eur. J. Mech. B. Fluids 55, 215–228 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  53. Xi, J., Yuan, J.E., Yang, M., Si, X., Zhou, Y., et al.: Parametric study on mouth-throat geometrical factors on deposition of orally inhaled aerosols. J. Aerosol. Sci. 99, 94–106 (2016)

    Article  Google Scholar 

  54. Zhou, Y., Guo, M., Xi, J., Irshad, H., Cheng, Y.S.: Nasal deposition in infants and children. J. Aerosol. Med. Pulm. Drug Deliv. 27, 110–116 (2014)

    Article  Google Scholar 

  55. Khosla, S., Murugappan, S., Lakhamraju, R., Gutmark, E.: Using particle imaging velocimetry to measure anterior-posterior velocity gradients in the excised canine larynx model. Ann. Otol. Rhinol. Laryngol. 117, 134–144 (2008)

    Article  Google Scholar 

  56. Saha, S., Moussavi, Z., Hadi, P., Bradley, T.D., Yadollahi, A.: Effects of increased pharyngeal tissue mass due to fluid accumulation in the neck on the acoustic features of snoring sounds in men. J. Clin. Sleep Med. JCSM Off. Publ. Am. Acad. Sleep Med. 14, 1653–1660 (2018)

    Google Scholar 

  57. Pasterkamp, H., Kraman, S.S., Wodicka, G.R.: Respiratory sounds: advances beyond the stethoscope. Am. J. Respir. Crit. Care Med. 156, 974–987 (1997)

    Article  Google Scholar 

  58. El Taoum, K.K., Xi, J., Kim, J., Berlinski, A.: In vitro evaluation of aerosols delivered via the nasal route. Respir. Care 60, 1015–1025 (2015)

    Article  Google Scholar 

  59. Xi, J., Si, X., Zhou, Y., Kim, J., Berlinski, A.: Growth of nasal and laryngeal airways in children: implications in breathing and inhaled aerosol dynamics. Respir. Care 59, 263–273 (2014)

    Article  Google Scholar 

  60. Xi, J., Kim, J., Si, X.A., Corley, R.A., Zhou, Y.: Modeling of inertial deposition in scaled models of rat and human nasal airways: towards in vitro regional dosimetry in small animals. J. Aerosol Sci. 99, 78–93 (2016)

    Article  Google Scholar 

  61. Xi, J., Si, X.A., Kim, J., Zhang, Y., Jacob, R.E., et al.: Anatomical details of the rabbit nasal passages and their implications in breathing, air conditioning, and olfaction. Anat. Rec. 299, 853–868 (2016)

    Article  Google Scholar 

  62. Patel, J.A., Ray, B.J., Fernandez-Salvador, C., Gouveia, C., Zaghi, S., et al.: Neuromuscular function of the soft palate and uvula in snoring and obstructive sleep apnea: a systematic review. Am. J. Otolaryngol. 39, 327–337 (2018)

    Article  Google Scholar 

  63. Pirnar, J., Dolenc-Grošelj, L., Fajdiga, I., Žun, I.: Computational fluid-structure interaction simulation of airflow in the human upper airway. J. Biomech. 48, 3685–3691 (2015)

    Article  Google Scholar 

  64. Zörner, S., Kaltenbacher, M., Döllinger, M.: Investigation of prescribed movement in fluid-structure interaction simulation for the human phonation process. Comput. Fluids 86, 133–140 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  65. Yang, J., Wang, X., Krane, M., Zhang, L.T.: Fully-coupled aeroelastic simulation with fluid compressibility: for application to vocal fold vibration. Comput. Methods Appl. Mech. Eng. 315, 584–606 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  66. Mittal, R., Zheng, X., Bhardwaj, R., Seo, J.H., Xue, Q., et al.: Toward a simulation-based tool for the treatment of vocal fold paralysis. Front. Physiol. 2, 19 (2011)

    Article  Google Scholar 

  67. Luo, H., Mittal, R., Bielamowicz, S.A.: Analysis of flow-structure interaction in the larynx during phonation using an immersed-boundary method. J. Acoust. Soc. Am. 126, 816–824 (2009)

    Article  Google Scholar 

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Funding

This study was funded by NSF CBET-1605232 (H.D.) and NSF Grant CBET 2001090 (J.X.).

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Massachusetts, 1 University Ave., Lowell, MA, 01854, USA

    Jinxiang Xi

  2. Department of Mechanical and Aerospace Engineering, Princeton Universit, Princeton, NJ, USA

    Junshi Wang

  3. Department of Aerospace, Industrial, and Mechanical Engineering, California Baptist University, Riverside, CA, USA

    Xiuhua April Si

  4. Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA

    Junshi Wang & Haibo Dong

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  1. Jinxiang Xi
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Contributions

JX and HD designed the study, JW and HD conducted the computational simulations and prepared Figs. 1, 2 and 3, XS and JX conducted the theoretical analyses and prepared Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13, and JX and XS wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Jinxiang Xi.

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Xi, J., Wang, J., Si, X.A. et al. Direct numerical simulations and flow-pressure acoustic analyses of flapping-uvula-induced flow evolutions within normal and constricted pharynx. Theor. Comput. Fluid Dyn. 37, 131–149 (2023). https://doi.org/10.1007/s00162-023-00638-1

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