| Autor: |
Panda B; Department of Biomedical Engineering, Case Western Reserve University.; Department of Electrical Engineering and Computer Science, Case Western Reserve University., Mandal S; Department of Electrical Engineering and Computer Science, Case Western Reserve University., Majerus SJA; Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH. |
| Jazyk: |
angličtina |
| Zdroj: |
... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium [IEEE Signal Process Med Biol Symp] 2019 Dec; Vol. 2019. Date of Electronic Publication: 2020 Mar 19. |
| DOI: |
10.1109/spmb47826.2019.9037853 |
| Abstrakt: |
Central venous stenosis is often undiagnosed in patients with hemodialysis vascular access, partly due to imaging difficulties. Noninvasive, point-of-care detection could rely on detecting regions of turbulent blood flow caused by blood velocity changes. Here we present flexible microphone arrays for time-correlated measures of blood flow sounds and a new signal processing approach to calculate time correlation between spectral features. Continuous wavelet transform was used to produce an auditory spectral flux analytic signal, which was thresholded to identify systolic start and end phases. Microphone arrays were tested on pulsatile flow phantoms with blood flow rates of 850-1,200 mL/min and simulated stenosis from 10-85%. Measured results showed an inversion in the time onset of systolic spectral content for sites proximal and distal to stenosis for hemodynamically significant stenoses (+22 ms for stenosis<50% and -20 to -38 ms for stenosis>50%). Equivalent blood velocity increases were calculated as 142-155 cm/s in stenotic phantoms, which are within the physiologic range as measured by ultrasound. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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