Advancing the Local Pulse Wave Velocity Measurement-Wave Confluence Decomposition Using a Double Gaussian Propagation Model

• Published in: IEEE transactions on biomedical engineering Vol. 71; no. 8; pp. 2495 - 2505
• Main Authors: Beutel, Fabian, Van Hoof, Chris, Hermeling, Evelien
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Analytical models; biological system modeling; Biomarkers; Biomedical measurement; biomedical signal processing; Blood pressure; Blood Pressure - physiology; Carotid Arteries - diagnostic imaging; Carotid Arteries - physiology; Centroids; Computational modeling; Correlation; Data models; Distension; Female; Fitting; Humans; Hypertension; Male; Middle Aged; Models, Cardiovascular; Normal Distribution; Propagation velocity; Pulse propagation; Pulse Wave Analysis - methods; Reflected waves; Reflection; Regression analysis; Signal Processing, Computer-Assisted; Spatiotemporal phenomena; ultrasonography; Vascular Stiffness - physiology; Velocity; Velocity measurement; Wave propagation; Wave velocity; Waveforms
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3378064

Background Pulse wave velocity (PWV) is a marker of arterial stiffness and local measurements could facilitate its widescale clinical use. However, confluence of incident and early reflected waves leads to biased spatiotemporal PWV estimates. Objective We introduce the Double Gaussian Propagation Model (DGPM) to measure local PWV in consideration of wave confluence (PWV<inline-formula><tex-math notation="LaTeX">_{\sf DGPM}</tex-math></inline-formula>) and compare it against conventional spatiotemporal PWV (PWV<inline-formula><tex-math notation="LaTeX">_{\sf ST}</tex-math></inline-formula>), with Bramwell-Hill PWV (PWV<inline-formula><tex-math notation="LaTeX">_{\sf BH}</tex-math></inline-formula>) and blood pressure (BP) as reference measures. Methods Ten subjects ranging from normotension to hypertension were repeatedly measured at rest and with induced PWV changes. Carotid distension waveforms over a 19 mm wide segment were acquired from ultrasonography, simultaneously with noninvasive continuous BP. Per cardiac cycle, the 8-parameter DGPM (amplitude, centroid, width, and velocity, respectively of forward and backward propagating wave) was fitted to the distension waveforms' systolic foot and dicrotic notch complexes. Corresponding PWV<inline-formula><tex-math notation="LaTeX">_{\sf ST}</tex-math></inline-formula> was computed from linear fittings of respective feature timings and distances. Regression analyses were conducted with PWV<inline-formula><tex-math notation="LaTeX">_{\sf DGPM}</tex-math></inline-formula> and PWV<inline-formula><tex-math notation="LaTeX">_{\sf ST}</tex-math></inline-formula> as predictors, and various PWV and BP measures as response variables. Results Whereas PWV<inline-formula><tex-math notation="LaTeX">_{\sf ST}</tex-math></inline-formula> correlations were insignificant, PWV<inline-formula><tex-math notation="LaTeX">_{\sf DGPM}</tex-math></inline-formula> estimated the reference PWV<inline-formula><tex-math notation="LaTeX">_{\sf BH}</tex-math></inline-formula> with a significant reduction in errors (P < 0.001), explained up to 65% PWV<inline-formula><tex-math notation="LaTeX">_{\sf BH}</tex-math></inline-formula> variability at rest, demonstrated higher intra-method consistency and correlated significantly with all BP measures (P < 0.001). Conclusion The proposed DGPM measures local carotid PWV in consideration of wave confluence, showing significant correlations with Bramwell-Hill PWV and BP at two distinct waveform complexes. Thereby PWV<inline-formula><tex-math notation="LaTeX">_{\sf DGPM}</tex-math></inline-formula> outperforms the conventional PWV<inline-formula><tex-math notation="LaTeX">_{\sf ST}</tex-math></inline-formula> in all investigated respects, potentially enabling PWV assessment in routine clinical practice.