A study of fluid dynamics parameters for prediction of obstructive sleep apnea

Kim, Hyoung Ho; Rakibuzzaman,; Suh, Sang Hyun; Kim, Hyun Jik; Choi, Jin Young; Lee, Ui‐Lyong

doi:10.1007/s12206-018-0210-0

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Physiologically based modeling has become a viral tool in studying the mechanisms of snore and OSA [16,17]. Relative to experimental testing, computational fluid dynamics and aeroacoustics can disclose great details of the vortex-dominated phenomenon both spatially and temporally [15,16,23,24,25,26,27]. Previous investigations have significantly improved our understanding of airway collapsibility and sound generation [15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

April

et al. 2023

Theor. Comput. Fluid Dyn.

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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 (FFT) 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 (CWT) showed clearly separated acoustic cycles (in both frequency and time) in the uvula-induced flows and revealed a cascading bifurcation pattern in the inputoutput 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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Section: Introductionmentioning

confidence: 99%

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

Wang

April

et al. 2023

Theor. Comput. Fluid Dyn.

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“…Vortex images, despite their vivid visual representations, are complex to interpret. It is still challenging to use these simulation results for diagnostic or prognostic purposes, e.g., to predict pharynx collapse, identify snoring source, or quantify tissue property variation [20,[23][24][25][26][27]. Further analysis of such data promises to reveal new information critical to understanding both respiratory physiology and pathology.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Discovery of Anomaly-Sensitive Parameters from Uvula Wake Flows Using Wavelet Analyses and Poincaré Maps

Si,

Wang,

Dong

et al. 2023

Acoustics

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This study presents a data-driven approach to identifying anomaly-sensitive parameters through a multiscale, multifaceted analysis of simulated respiratory flows. The anomalies under consideration include a pharyngeal model with three levels of constriction (M1, M2, M3) and a flapping uvula with two types of kinematics (K1, K2). Direct numerical simulations (DNS) were implemented to solve the wake flows induced by a flapping uvula; instantaneous vortex images, as well as pressures and velocities at seven probes, were recorded for twelve cycles. Principal component analysis (PCA), wavelet-based multifractal spectrum and scalogram, and Poincaré mapping were implemented to identify anomaly-sensitive parameters. The PCA results demonstrated a reasonable periodicity of instantaneous vortex images in the leading vector space and revealed distinct patterns between models with varying uvula kinematics (K1, K2). At higher PCA ranks, the periodicity gradually decays, eventually transitioning to a random pattern. The multifractal spectra and scalograms of pressures in the pharynx (P6, P7) show high sensitivity to uvula kinematics, with the pitching mode (K2) having a wider spectrum and a left-skewed peak than the heaving mode (K1). Conversely, the Poincaré maps of velocities and pressures in the pharynx (Vel6, Vel7, P6, P7) exhibit high sensitivity to pharyngeal constriction levels (M1–M3), but not to uvula kinematics. The parameter sensitivity to anomaly also differs with the probe site; thus, synergizing measurements from multiple probes with properly extracted anomaly-sensitive parameters holds the potential to localize the source of snoring and estimate the collapsibility of the pharynx.

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“…Although extensive numerical studies on flow patterns in the human airways have been conducted [8][9][10][11], few researchers have explored the URT flow of OSA patients using the CFD [12][13][14][15]. Since the morphology of realistic OSA URT is very complex, the flow in it is expected to be turbulent [16].…”

Section: Introductionmentioning

confidence: 99%

Flow Simulation in the Upper Respiratory Tract of Two Obstructive Sleep Apnea Patients with Successful and Failed Surgery

Shao

Yan

Liu

et al. 2021

Computational and Mathematical Methods in Medicine

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Obstructive sleep apnea (OSA) is a common disorder which may need to be treated by the upper respiratory tract (URT) surgery. To increase the success rate of the URT surgery, it is crucial to understand the flow features in the URT models. In this work, the turbulent flow characteristics in four 3D anatomically accurate URT models reconstructed from two OSA subjects with successful and failed surgery are numerically studied by the large-eddy simulation (LES) and unsteady Reynolds-averaged Navier-Stokes (RANS). The features of velocity fields, pressure fields, and wall shear stress fields as well as the spectral analysis of wall shear stress between successful and failed surgery are explored. The results indicate that LES is capable of capturing flow patterns and flow oscillation and is effective for OSA surgery prediction. Even if the unsteady RANS can obtain the correct pressure drop across the airways, it may not be appropriate to be used for surgery prediction. Moreover, it is found that the quality of oscillating signal of wall shear stress is a key factor in surgery prediction. In a successful surgery, the wall shear stress oscillation is always strong, and the oscillating signal can perform a dominant frequency near 3~5 Hz, while in a failed surgery it does not show this clear intrinsic property. The results not only will gain new insights in the URT surgical planning but also will improve the prediction of surgical outcome for OSA patients.

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A study of fluid dynamics parameters for prediction of obstructive sleep apnea

Cited by 7 publications

References 17 publications

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

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

Data-Driven Discovery of Anomaly-Sensitive Parameters from Uvula Wake Flows Using Wavelet Analyses and Poincaré Maps

Flow Simulation in the Upper Respiratory Tract of Two Obstructive Sleep Apnea Patients with Successful and Failed Surgery

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