Spectral Decomposition and Sound Source Localization of Highly Disturbed Flow through a Severe Arterial Stenosis

Abstract

For the early detection of atherosclerosis, it is imperative to explore the capabilities of new, effective noninvasive diagnosis techniques to significantly reduce the associated treatment costs and mortality rates. In this study, a multifaceted comprehensive approach involving advanced computational fluid dynamics combined with signal processing techniques was exploited to investigate the highly turbulent fluctuating flow through arterial stenosis. The focus was on localizing high-energy mechano-acoustic source potential to transmit to the epidermal surface. The flow analysis results showed the existence of turbulent pressure fluctuations inside the stenosis and in the post-stenotic region. After analyzing the turbulent kinetic energy and pressure fluctuations on the flow centerline and the vessel wall, the point of maximum excitation in the flow was observed around two diameters downstream of the stenosis within the fluctuating zone. It was also found that the concentration of pressure fluctuation closer to the wall was higher inside the stenosis compared to the post-stenotic region. Additionally, the visualization of the most energetic proper orthogonal decomposition (POD) mode and spectral decomposition of the flow indicated that the break frequencies ranged from 80 to 220 Hz and were correlated to the eddies generated within these regions.

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

“…As widely showed in the literature concerning heart rate variability [ 1 , 2 , 3 , 4 ], computerized signal processing techniques proved to be an effective way to detect different types of cardiovascular disorders. Indeed, the processing and computerized analysis of heart rate signals, with particular regard to the heart rate variability (HRV), contributed to enhance the diagnostic value brought by the clinical diagnostic techniques, by providing additional information and data that can support the clinical decision-making and leading to standardized, reliable, and early diagnoses [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ].…”