Search Results (4)

This paper proposes a non-contact continuous respiration monitoring method based on Fringe Projection Profilometry (FPP). This method aims to overcome the limitations of traditional intrusive techniques by providing continuous monitoring without interfering with normal breathing. The FPP sensor captures three-dimensional (3D) respiratory motion from the chest wall and abdomen, and the analysis algorithms extract respiratory parameters. The system achieved a high Signal-to-Noise Ratio (SNR) of 37 dB with an ideal sinusoidal respiration signal. Experimental results demonstrated that a mean correlation of 0.95 and a mean Root-Mean-Square Error (RMSE) of 0.11 breaths per minute (bpm) were achieved when comparing to a reference signal obtained from a spirometer. Full article

Glottal vibration is fundamental to breathing-related disorders and respiratory sound generation. However, responses of the flow and acoustics to glottal vibrations of different frequencies are unclear. The objective of this study is to numerically evaluate the influences of glottal vibration frequencies on inspiratory airflow dynamics and flow-induced sound signals; this is different from normal phonation that is driven by controlled expiratory flows. A computational model was developed that comprised an image-based mouth–throat–lung model and a dynamic glottis expanding/contracting following a sinusoidal waveform. Large Eddy simulations were used to solve the temporal and spatial flow evolutions, and pressure signals were analyzed using different transform algorithms (wavelet, Hilbert, Fourier, etc.). Results show that glottal vibrations significantly altered the flows in the glottis and trachea, especially at high frequencies. With increasing vibration frequencies, the vortices decreased in scale and moved from the main flow to the walls. Phase shifts occurred between the glottis motion and glottal flow rates for all frequencies considered. Due to this phase shift, the pressure forces resisted the glottal motion in the first half of contraction/expansion and assisted the glottal motion in the second half of contraction/expansion. The magnitude of the glottal flow fluctuation was approximately linear with the vibration frequency (~f₀), while the normal pressure force increased nonlinearly with the frequency (~f₀^1.85). Instantaneous pressure signals were irregular at low vibration frequencies (10 and 20 Hz) but became more regular with increasing frequencies in the pressure profile, periodicity, and wavelet-transformed parameters. The acoustic characteristics specific to the glottal vibration frequency were explored in temporal and frequency domains, which may be used individually or as a combination in diagnosing vocal fold dysfunction, snoring, sleep apnea, or other breathing-related diseases. Full article

Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset. Full article

An airflow in the first four generations of the tracheobronchial tree was simulated by the 1D model of incompressible fluid flow through the network of the elastic tubes coupled with 0D models of lumped alveolar components, which aggregates parts of the alveolar volume and smaller airways, extended with convective transport model throughout the lung and alveolar components which were combined with the model of oxygen and carbon dioxide transport between the alveolar volume and the averaged blood compartment during pathological respiratory conditions. The novel features of this work are 1D reconstruction of the tracheobronchial tree structure on the basis of 3D segmentation of the computed tomography (CT) data; 1D−0D coupling of the models of 1D bronchial tube and 0D alveolar components; and the alveolar gas exchange model. The results of our simulations include mechanical ventilation, breathing patterns of severely ill patients with the cluster (Biot) and periodic (Cheyne-Stokes) respirations and bronchial asthma attack. The suitability of the proposed mathematical model was validated. Carbon dioxide elimination efficiency was analyzed in all these cases. In the future, these results might be integrated into research and practical studies aimed to design cyberbiological systems for remote real-time monitoring, classification, prediction of breathing patterns and alveolar gas exchange for patients with breathing problems. Full article