Pulse Wave Velocity Estimation Research Articles

Optimum Parameters in Ultrasound Coherent Plane Wave Compounding for High LPWV Estimation: Validation on Phantom and Feasibility in 10 Subjects.

The aim of this study is to determine the optimum and fine values of the number and transmission angles of tilted plane waves for coherent plane-wave compounding (CPWC)-based high local pulse wave velocity (LPWV) estimation. A Verasonics system incorporating a linear array probe L14-5/38 with 128 elements and a pulsatile pump, CompuFlow1000, were used to acquire radio frequency data of 3, 5, 7, and 9 tilted plane wave sequences with angle intervals from 0° to 12° with a coarse interval increment step of 1°, and the angle intervals from 0° to 2° with a fine interval increment step of 0.25° from a carotid vessel phantom with the LPWV of 13.42 ± 0.90 m/s. The mean value, standard deviation, and coefficients of variation (CV) of the estimated LPWVs were calculated to quantitatively assess the performance of different configurations for CPWC-based LPWV estimation. Ten healthy human subjects of two age groups were recruited to assess the in vivo feasibility of the optimum parameter values. The CPWC technique with three plane waves (PRF of 12 kHz corresponding to a frame rate of 4000 Hz) with an interval of 0.75° had LPWVs of 13.52 ± 0.08 m/s with the lowest CV of 1.84% on the phantom, and 5.49 ± 1.46 m/s with the lowest CV of 12.35% on 10 subjects. The optimum parameters determined in this study show the best repeatability of the LPWV measurements with a vessel phantom and 10 healthy subjects, which support further studies on larger datasets for potential applications.

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

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 (PWVDGPM) and compare it against conventional spatiotemporal PWV (PWVST), with Bramwell-Hill PWV (PWVBH) 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 PWVST was computed from linear fittings of respective feature timings and distances. Regression analyses were conducted with PWVDGPM and PWVST as predictors, and various PWV and BP measures as response variables. Results Whereas PWVST correlations were insignificant, PWVDGPM estimated the reference PWVBH with a significant reduction in errors (P<0.001), explained up to 65% PWVBH 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 PWVDGPM outperforms the conventional PWVST in all investigated respects, potentially enabling PWV assessment in routine clinical practice.

Value of estimating pulse wave velocity compared to SCORE in cardiovascular risk stratification in community pharmacies

ObjectivesArterial stiffness is considered to be an intermediate marker with independent prognostic value. The objective of this study is to assess whether the estimation of arterial stiffness can improve CV risk stratification compared to SCORE in patients at community pharmacies. MethodsObservational prospective epidemiological study in which consecutive individuals entering a participating Community Pharmacy are offered a voluntary measurement of blood pressure and estimation of pulse wave velocity by oscillometry (AGEDIO, IEM®) to stratify their CV risk according to SCORE compared to the use of arterial stiffness. ResultsAfter nine months of recruitment, data from 923 patients (570 women, 353 men) were collected. 16/122 (13.1%) patients under 40 years and 72/364 (19.8%) over 65 years of age presented pathological stiffness and could be classified as high-risk, even though being out of the age-range of SCORE. Of the 437 (47.3%) patients who were susceptible to calculating SCORE, 42/437 patients (9.6%) presented pathological arterial stiffness. Cholesterol values were available in 281 patients (64.3%). Among them, according to SCORE, only 6 (2.1%) fell into the high-risk category. ConclusionsMore than half of the subjects who randomly enter a community pharmacy had ages that make it impossible to calculate the CV risk by SCORE. Among them, arterial damage was detected in 18.1%. Of the other half, 9.6% presented arterial damage and, therefore, high CV risk, when SCORE only detected it in 2.1%. Therefore, estimating arterial stiffness in community pharmacies markedly improves detection of high CV risk compared to SCORE.

Polyvinyl Alcohol Phantoms With Heterogeneous Plaques: Estimation of Pulse Wave Velocity at the Stenotic Region Using Pulse Wave Imaging

Plaque characterization is essential for stroke prevention. In the study reported herein, we describe a heterogeneous phantom manufacturing technique with varying plaque compositions of different stiffness using polyvinyl alcohol (PVA) to emulate stenotic arteries and evaluated the use of pulse wave imaging (PWI) to assess plaque stiffness by comparing derived pulse wave velocities, with the goal of assessing plaque vulnerability and identifying high-risk patients for stroke. Five stenotic phantoms (50% stenosis) were fabricated by pouring PVA solutions into 3-D-printed molds. Two of the phantoms had heterogeneous plaque compositions of soft (E0=13 kPa) and intermediate (E0=40 kPa) materials and of stiff (E0=54 kPa) and intermediate materials. Ultrasound sequences were acquired as the arterial phantoms were connected to a pulsating pump, and PWI was performed on the ultrasound acquisition using normalized cross-correlation to track the pulse-induced phantom wall distension propagations. Pulse wave velocities were estimated by fitting a linear regression line between the arrival time of the peak acceleration of the wall distension waveform and the corresponding location. Arterial phantoms with heterogeneous plaque stiffness were successfully fabricated. Pulse wave velocities of 2.06, 2.21, 2.49, 2.67 and 3.31 m/s were found in the phantom experiments using PWI for homogeneous soft plaque, the heterogeneous soft and intermediate plaque, homogeneous intermediate plaque, the heterogeneous stiff and intermediate plaque and homogeneous stiff plaque, respectively. A novel arterial phantom building technique was reported with varying heterogenous plaque compositions of different stiffness. The feasibility of using PWI to evaluate plaque stiffness in stenotic arteries was determined and found that PWI can distinguish between plaques of distinct stiffness and composition.

Interpolation time-optimized aortic pulse wave velocity estimation by 4D flow MRI

Four-dimensional flow magnetic resonance imaging-based pulse wave velocity (4D flow PWV) estimation is a promising tool for measuring regional aortic stiffness for non-invasive cardiovascular disease screening. However, the effect of variations in the shape of flow waveforms on 4D flow PWV measurements remains unclear. In this study, 4D flow PWV values were compared using cross-correlation algorithm with different interpolation times (iTs) based on flow rate and beat frequency. A critical iT (iTCrit) was proposed from in vitro study using flexible and stiff phantom models to simultaneously achieve a low difference and a low computation time. In vivo 4D flow PWV values from six healthy volunteers were also compared between iTCrit and the conventionally used interpolation time of 1 ms (iT1 ms). The results indicated that iTCrit reduced the mean difference of in vitro 4D flow PWV values by 19%, compared to iT1 ms. In addition, iTCrit measured in vivo 4D flow PWV, showing differences similar to those obtained with iT1 ms. A difference estimation model was proposed to retrospectively estimate potential differences of 4D flow PWV using known values of PWV and the used iT. This study would be helpful for understanding the differences of PWV generated by physiological changes and time step of obtained flow waveforms.

Value of estimating pulse wave velocity compared to SCORE in cardiovascular risk stratification in community pharmacies

ObjectivesArterial stiffness is considered to be an intermediate marker with independent prognostic value. The objective of this study is to assess whether the estimation of arterial stiffness can improve CV risk stratification compared to SCORE in patients at community pharmacies. MethodsObservational prospective epidemiological study in which consecutive individuals entering a participating Community Pharmacy are offered a voluntary measurement of blood pressure and estimation of pulse wave velocity by oscillometry (AGEDIO, IEM®) to stratify their CV risk according to SCORE compared to the use of arterial stiffness. ResultsAfter nine months of recruitment, data from 923 patients (570 women, 353 men) were collected. 16/122 (13.1%) patients under 40 years and 72/364 (19.8%) over 65 years of age presented pathological stiffness and could be classified as high-risk, even though being out of the age-range of SCORE. Of the 437 (47.3%) patients who were susceptible to calculating SCORE, 42/437 patients (9.6%) presented pathological arterial stiffness. Cholesterol values were available in 281 patients (64.3%). Among them, according to SCORE, only 6 (2.1%) fell into the high-risk category. ConclusionsMore than half of the subjects who randomly enter a community pharmacy had ages that make it impossible to calculate the CV risk by SCORE. Among them, arterial damage was detected in 18.1%. Of the other half, 9.6% presented arterial damage and, therefore, high CV risk, when SCORE only detected it in 2.1%. Therefore, estimating arterial stiffness in community pharmacies markedly improves detection of high CV risk compared to SCORE.

BEAT-TO-BEAT VARIABILITY OF INVASIVE PULSE WAVE VELOCITY: TECHNICAL AND CLINICAL ASPECTS

Objective: Invasive foot-to-foot pulse wave velocity (PWV) is the reference standard for validating new non-invasive PWV-measuring devices. Because of acute haemodynamic variability and data processing issues, PWV shows beat-to-beat variability, which may have important clinical and technical implications. Though current device validation guidelines suggest averaging over at least n = 3 heartbeats to cope with variability, quantitative data on PWV's beat-to-beat variability is lacking. We aimed to quantify this variability and its impact on the confidence of the PWV estimate. Design and method: Pressure waveforms were acquired in n = 82 individuals in the ascending aorta and iliac bifurcation using a dual-tip intra-aortic catheter. For each individual, two datasets were recorded: 1) simultaneous ascending and iliac pressure acquisitions (with ECG), and 2) an additional ascending aorta pressure acquisition (with ECG). Pressure wave foot detection was performed using the intersecting interpolant method. By combining the iliac pressure recording from the first acquisition with the ascending aorta pressure recording from the second acquisition, we evaluated how a sequential acquisition protocol (e.g., catheter pull-back) affects the variability. Furthermore, we estimated how averaging over n consecutive heartbeats affects the standard deviation (SD) of such n-beats-averaged estimate of PWV. Results: PWV was 10.93 ± 4.24 and 11.12 ± 4.42 m/s (mean ± between-subject SD) for simultaneous (PWVsim) and sequential acquisitions (PWVseq; p = 0.002), respectively. The average within-subject SD ( = variability) was 0.48 ± 0.28 m/s for PWVsim and 1.47 ± 1.20 m/s for PWVseq (p &lt; 0.001). This increase in variability was mainly due to the beat-to-beat variability of the ECG R-wave-to-pressure foot time interval, which was ∼2 times higher than that of the simultaneous foot-to-foot transit time (Figure A). Averaging over n = 3 reduced the variability of PWVsim to 0.37 ± 0.25 m/s, while n = 10.24 (median) is required to reach a comparable variability for PWVseq (Figure B). Conclusions: Variability of an invasive foot-to-foot PWV estimate increases considerably when using sequential rather than simultaneous acquisitions. This increase is caused by the high beat-to-beat variability of the duration of the pre-ejection period. To reduce the effective variability of a sequential acquisition to the same level as that of a simultaneous acquisition, averaging over an approximately threefold higher number of heartbeats is required.

A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study.

Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in the clinical routine, the measurement process of carotid-to-femoral pulse wave velocity is considered a demanding task for clinicians and patients making it prone to inaccuracies and errors in the estimation. A smart non-invasive, and peripheral measurement of carotid-to-femoral pulse wave velocity could overcome the challenges of the classical assessment process and improve the quality of patient care. This paper proposes a novel methodology for the carotid-to-femoral pulse wave velocity estimation based on the use of the spectrogram representation from single non-invasive peripheral pulse wave signals [photoplethysmography (PPG) or blood pressure (BP)]. This methodology was tested using three feature extraction methods based on the semi-classical signal analysis (SCSA) method, the Law's mask for texture energy extraction, and the central statistical moments. Finally, each feature method was fed into different machine learning models for the carotid-to-femoral pulse wave velocity estimation. The proposed methodology obtained an $R2\geq0.90$ for all the peripheral signals for the noise-free case using the MLP model, and for the different noise levels added to the original signal, the SCSA-based features with the MLP model presented an $R2\geq0.91$ for all the peripheral signals at the level of noise. These results provide evidence of the capacity of spectrogram representation for efficiently assessing the carotid-to-femoral pulse wave velocity estimation using different feature methods. Future work will be done toward testing the proposed methodology for in-vivo signals.

Pulse wave imaging of a stenotic artery model with plaque constituents of different stiffnesses: Experimental demonstration in phantoms and fluid-structure interaction simulation

Vulnerable plaques associated with softer components may rupture, releasing thrombotic emboli to smaller vessels in the brain, thus causing an ischemic stroke. Pulse Wave Imaging (PWI) is an ultrasound-based method that allows for pulse wave visualization while the regional pulse wave velocity (PWV) is mapped along the arterial wall to infer the underlying wall compliance. One potential application of PWI is the non-invasive estimation of plaque’s mechanical properties for investigating its vulnerability. In this study, the accuracy of PWV estimation in stenotic vessels was investigated by computational simulation and PWI in validation phantoms to evaluate this modality for assessing future stroke risk. Polyvinyl alcohol (PVA) phantoms with plaque constituents of different stiffnesses were designed and constructed to emulate stenotic arteries in the experiment, and the novel fabrication process was described. Finite-element fluid–structure interaction simulations were performed in a stenotic phantom model that matched the geometry and parameters of the experiment in phantoms. The peak distension acceleration of the phantom wall was tracked to estimate PWV. PWVs of 2.57 ms−1, 3.41 ms−1, and 4.48 ms−1 were respectively obtained in the soft, intermediate, and stiff plaque material in phantoms during the experiment using PWI. PWVs of 2.10 ms−1, 3.33 ms−1, and 4.02 ms−1 were respectively found in the soft, intermediate, and stiff plaque material in the computational simulation. These results demonstrate that PWI can effectively distinguish the mechanical properties of plaque in phantoms as compared to computational simulation.

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach.

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure-strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure-strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm2, respectively. Regarding the pressure-strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure-strain loop of central arteries while observing pressure signals from peripheral arteries.

What Is the Smallest Change in Pulse Wave Velocity Measurements That Can Be Attributed to Clinical Changes in Arterial Stiffness with Certainty: A Randomized Cross-Over Study

Pulse wave velocity (PWV), a direct measure of arterial stiffness, is a promising biomarker of cardiovascular risk and a cardiovascular surrogate outcome. The resolution for detecting its smallest clinically significant change is dependent on the expected reproducibility, but there is currently no consensus on this. We estimated the PWV reproducibility in a range of intra-subject values that were observed over a 2 week period in a broad range of participants and under clinically relevant experimental conditions (two observers, morning/afternoon sessions, and number of visits) using SphygmoCor and Arteriograph devices. Each participant was recorded 12 times with each device over three visits, one week apart, and two morning and two afternoon recordings were taken per visit. The factors affecting reproducibility and the discrepancies between the consecutive PWV measurements for each device were also examined using multilevel mixed-effect models. We show that current PWV estimation guidance recommending 2 + 1 measurements is suboptimal because the PWV range was outside of the 1 m/s threshold for most of the participants, which is proposed as a minimal clinically important difference. The best reproducibility was yielded with median of four measurements and a 1.1 m/s threshold. Although PWV reproducibility and repeatability are frequently used interchangeably in studies, we demonstrated that despite their relative measures of variability (e.g., coefficient of variation) being comparable, their ranges revealed a clinically significant difference between them. We also found that different physiological variables were predictors of the discrepancy between the consecutive measurements made by the two devices, which is likely due to their distinct modes of operation. The evidence base for PWV reproducibility is limited, and more research is needed to deepen our understanding of the variation in arterial stiffness over time, as well as fluctuations within a population group and in an intervention setting.

Efficacy of ultrasound vector flow imaging in tracking omnidirectional pulsatile flow.

Ultrasound vector flow imaging (VFI) shows potential as an emerging non-invasive modality for time-resolved flow mapping. However, its efficacy in tracking multidirectional pulsatile flow with temporal resolvability has not yet been systematically evaluated because of the lack of an appropriate test protocol. We present the first systematic performance investigation of VFI in tracking pulsatile flow in a meticulously designed scenario with time-varying, omnidirectional flow fields (with flow angles from 0° to 360°). Ultrasound VFI was performed on a three-loop spiral flow phantom (4mm diameter; 5mm pitch) that was configured to operate under pulsatile flow conditions (10ml/s peak flow rate; 1Hz pulse rate; carotid pulse shape). The spiral lumen geometry was designed to simulate recirculatory flow dynamics observed in the heart and in curvy blood vessel segments such as the carotid bulb. The imaging sequence was based on steered plane wave pulsing (-10°, 0°, +10° steering angles; 5MHz imaging frequency; 3.3kHz interleaved pulse repetition frequency). VFI's pulsatile flow estimation performance and its ability to detect secondary flow were comparatively assessed against flow fields derived from computational fluid dynamics (CFD) simulations that included consideration of fluid-structure interactions (FSI). The mean percentage error (MPE) and the coefficient of determination (R2 ) were computed to assess the correspondence of the velocity estimates derived from VFI and CFD-FSI simulations. In addition, VFI's efficacy in tracking pulse waves was analyzed with respect to pressure transducer measurements made at the phantom's inlet and outlet. Pulsatile flow patterns rendered by VFI agreed with the flow profiles computed from CFD-FSI simulations (average MPE: -5.3%). The shape of the VFI-measured velocity magnitude profile generally matched the inlet flow profile. High correlation exists between VFI measurements and simulated flow vectors (lateral velocity: R2 =0.8; axial velocity R2 =0.89; beam-flow angle: R2 =0.98; p<0.0001 for all three quantities). VFI was found to be capable of consistently tracking secondary flow. It also yielded pulse wave velocity (PWV) estimates (5.72±1.02m/s) that, on average, are within 6.4% of those obtained from pressure transducer measurements (6.11±1.15m/s). VFI can consistently track omnidirectional pulsatile flow on a time-resolved basis. This systematic investigation serves well as a quality assurance test of VFI.

PS-C03-12: FINGER-TO-TOE PULSE WAVE VELOCITY IN RESPONSE TO SITTING POSITION

Objective: Aortic stiffness is a non-traditional risk factor for cardiovascular disease and mortality and is associated with increased central pulse pressure. It is generally measured by carotid-femoral pulse wave velocity (PWV) in a supine position. We hypothesized that, in a sitting position, the additional hydrostatic pressure exerted on the vascular wall could represent a vascular stress test. Two alternative methods can now estimate aortic stiffness and can be used conveniently in different positions: 1) brachial pulse wave analysis approach used by Mobil-O-Graph and 2) transit time approach using finger-to-toe PWV by photoplethysmography with the pOpmetre. The aim of the present study is to determine the impact of postural changes on PWV and central blood pressure estimation using these two distinct methods. Design and method: Central blood pressure and PWV were assessed in 20 healthy subjects using Mobil-O-Graph (PWV-M) and pOpmetre (PWV-pop) in three positions: supine in bed, supine in reclining chair and sitting. Central systolic blood pressure (SBP) was determined by calibration of brachial systolic and diastolic blood pressures by both methods (SBP-M and SBP-pop). Generalized estimating equations were used for analysis. Results: The PWV-M remained relatively constant and generally independent of the position (5.5 ± 1.0, 5.3 ± 1.0 and 5.5 ± 1.0 m/s). While PWV-M and PWV-pop were similar in the supine position (5.5 ± 1.0 vs 5.4 ± 1.3 m/s, P = NS), PWV-pop increased very significantly to 5.9 ± 1.5 m/s in supine in reclining chair position (P = 0.04), and to 9.2 ± 1.7 m/s in sitting position (P &lt; 0.001). Central blood pressure estimation remained similar for SBP-M (110.2 ± 10.3 vs 106.9 ± 10.5 vs 108.8 ± 11.1 mmHg, P = NS) and for SBP-pop (107.7 ± 10.4 vs 110.3 ± 9.6 vs 109.5 ± 9.9 mmHg, P = NS). Conclusions: These results suggest that estimated PWV by brachial pulse wave analysis remains robust independent of position. However, the estimated finger-to-toe PWV by pulse transit time increases remarkably with the imposition of hydrostatic pressure on the vascular wall. We conclude that the finger-to-toe PWV may provide the opportunity to evaluated vascular wall property in a dynamic fashion using postural changes as a stress test.

In Vivo Comparison of Pulse Wave Velocity Estimation Based on Ultrafast Plane Wave Imaging and High-Frame-Rate Focused Transmissions.

Ultrasound-based local pulse wave velocity (PWV) estimation, as a measure of arterial stiffness, can be based on fast focused imaging (FFI) or plane wave imaging (PWI). This study was aimed at comparing the accuracy of in vivo PWV estimation using FFI and PWI. Ultrasound radiofrequency data of carotid arteries were acquired in 14 healthy volunteers (25-57 y) by executing the FFI (12 lines, 7200 Hz) and PWI (128 lines, 2000 Hz) methods consecutively. PWV was derived at two time-reference points, dicrotic notch (DN) and systolic foot (SF), for multiple pressure cycles by fitting a linear function through the positions of the peaks of low-pass filtered wall acceleration curves as a function of time. The accuracy of PWV estimation was determined for various cutoff frequencies (10-200 Hz). No statistically significant difference was observed between PWVs estimated by both approaches. The PWV and R2 at DN were higher, on average, than those at SF (PWV/R2: FFI SF 5.5/0.92, FFI DN 6.1/0.92; PWI SF 5.4/0.89, PWI DN 6.3/0.95). The use of cutoff frequencies between 40 and 80Hz provided the most accurate PWVs. Both methods seemed equally suitable for use in clinical practice, although we have a preference for the PWV at DN given the higher R2 values.

Impact of estimated pulse wave velocity on cardiovascular outcomes in a general population

Abstract Background Estimated pulse wave velocity (ePWV), a simple surrogate estimate of carotid-femoral pulse wave velocity which was gold standard for measuring aortic stiffness, has been demonstrated to predict cardiovascular (CV) outcome. However, there was limited data on its predictive capacity for CV outcomes in the general population. The study aim was to investigate the independent association between ePWV and CV outcomes in general population. Methods A total of 10,030 subjects aged between 40 and 69 years were followed over 18-year period in the Ansan-Ansung cohort study. Levels of ePWV, which was calculated from an equation based on age and mean blood pressure, were categorized according to quartiles. A major adverse cardiovascular event (MACE) was defined as a composite of myocardial infarction, coronary artery disease, stroke, heart failure, peripheral artery disease, and cardiovascular death. Results The incidence rates of CV death, and MACE were 7.0% and 22.1% in the fourth (highest) quartile of ePWV and 0.1% and 4.5% in the first (lowest) quartile of ePWV. After adjusting for relevant covariates, patients with third and fourth quartile of ePWV showed significantly higher risk of CV death (hazard ratio [HR] 3.92; 95% confidence intervals [CI] 1.09–14.15 in third quartile and HR 8.53; 95% CI 2.13–34.10 in fourth quartile), and MACE (HR 1.54; 95% CI 1.15–2.08 in third quartile and HR 1.56; 95% CI 1.05–2.32) compared to the reference of first (lowest) quartile of ePWV. However, there were no improvement of C statistic for CV death and MACE when adding ePWV to the Cox regression models with 10-year atherosclerotic cardiovascular disease (ASCVD) risk. Conclusion These results suggest that ePWV predicted CV death and MACE in general population. The value of ePWV, a simple and useful indicator of aortic stiffness, is expected to serve as a potential marker to identify high risk groups of CV event in general population. Funding Acknowledgement Type of funding sources: None.

Estimation of carotid-femoral pulse wave velocity from finger photoplethysmography signal

Objective. This project compared a new method to estimate the carotid-femoral pulse wave velocity (cf-PWV) to the gold-standard cf-PWV technique. Approach. The cf-PWV was estimated from the pulse transit time (FPS-PTT) calculated by processing the finger photoplethysmographic signal of Finapres (FPS) and subject’s height only (brief mode) as well as along with other variables (age, heart rate, arterial pressure, weight; complete mode). Doppler ultrasound cf-PWVs and FPS-PTTs were measured in 90 participants equally divided into 3 groups (18–30; 31–59; 60–79 years). Predictions were performed using multiple linear regressions (MLR) and with the best regression model identified by using MATLAB Regression Learner App. A validation set approach (60 training datasets, 30 testing datasets; VSA) and leave-one-out cross-validation (LOOCV) were used. Main results. With MLR, the discrepancies were: 0.01 ± 1.21 m s−1 (VSA) and 0.001 ± 1.11 m s−1 (LOOCV) in brief mode; −0.02 ± 0.83 m s−1 (VSA) and 0.001 ± 0.84 m s−1 (LOOCV) in complete mode. Using a linear support vector machine model (SVM) in brief mode, the discrepancies were: 0.01 ± 1.19 m s−1 (VSA) and −0.01 ± 1.06 m s−1 (LOOCV). Using an Exponential Gaussian process regression model (GPR) in complete mode, the discrepancies were: −0.03 ± 0.79 m s−1 (VSA) and 0.01 ± 0.75 m s−1 (LOOCV). Significance. The cf-PWV can be estimated by processing the FPS-PTT and subjects’ height only, but the inclusion of other variables improves the prediction performance. Predictions through MLR qualify as acceptable in both brief and complete modes. Predictions via linear SVM in brief mode improve but still qualify as acceptable. Interestingly, predictions through Exponential GPR in complete mode improve and qualify as excellent.

IMPACT OF POSTURAL POSITION ON DETERMINATION OF AORTIC STIFFNESS AND CENTRAL BLOOD PRESSURE

Objective: Aortic stiffness is associated with increased central pulse pressure and cardiovascular disease and mortality. It is generally performed by determination of carotid-femoral pulse wave velocity (PWV) in a supine position. Two alternative approaches have been developed to estimate aortic stiffness, namely brachial pulse waveform analysis (Mobil-O-Graph) and finger-to-toe PWV by photoplethysmography (pOpmeter). These devices make it possible to assess and estimate aortic stiffness conveniently in different positions. However, it remains unclear if the values obtained are independent of the position at the time of measurement. Therefore, the aim of the present study is to determine the extent of postural changes on pulse wave velocity and central blood pressure estimation using pOpmetre and Mobil-O-Graph devices in a group of healthy controls. Design and method: Central blood pressure and PWV were assessed in 20 healthy subjects using both Mobil-O-Graph (PWV-M) and pOpmetre (PWV-pop) in three positions: supine position in bed, supine position in medical sampling chair and a sitting position. Central systolic blood pressure (SBP) was determined by calibration of brachial systolic and diastolic blood pressures (C1 calibration) by both methods (SBP-M and SBP-pop). Results: In the supine position in bed, PWV-M and PWV-pop were similar (5.5 ± 1.0 vs 5.4 ± 1.3 m/s, P = NS). In the supine position in a sampling chair, PWV-M decreased slightly (5.3 ± 1.0 m/s, P = 0.04 vs supine in bed), while PWV-pop increased significantly (5.9 ± 1.5 m/s, P = 0.04 vs supine in bed). However, in the sitting position, while PWV-M was not statistically different from the supine position (5.5 ± 1.0 m/s, P = NS), PWV-pop increased very significantly (9.2 ± 1.7 m/s, P &lt; 0.001). Central blood pressure estimation remained similar for SBP-M (110.2 ± 10.3 vs 106.9 ± 10.5 vs 108.8 ± 11.1 mmHg, P = NS) and for SBP-pop (107.7 ± 10.4 vs 110.3 ± 9.6 vs 109.5 ± 9.9 mmHg, P = NS). Conclusions: These preliminary results tell us that postural position has little impact on the determination of aortic stiffness by the approach using brachial pulse wave analysis. However, finger-to-toe PWV increases remarkably with changes in position, possibly offering to explore the relationship between hydrostatic pressure and arterial stiffness as a dynamic test.

Pulse wave propagation through a stenotic artery with plaque constituents of different stiffnesses: In vitro-experiment and fluid–structure interaction simulation

Vulnerable (typically, softer) carotid plaques may rupture leading to blockages downstream in the brain with high potential for ischemic stroke. Pulse Wave Imaging (PWI) developed by our group is an ultrasound-based technique for pulse wave visualization and pulse wave velocity (PWV) mapping. In this study, the accuracy of PWV estimation in stenotic vessels was assessed in simulation and phantom studies. Fluid–structure interaction simulations were performed in a stenotic (50%) vessel. Experimental stenotic (50%) polyvinyl alcohol (PVA) phantoms (2 mm thickness, 8 mm lumen diameter, and 165 mm length) with soft/medium/hard plaque constituents of different stiffness (E0 = 12 kPa, 30 kPa, 94 kPa) were constructed matching the simulation geometry and parameters. Pulse waves were generated by a computer-controlled pump using a physiologic flow waveform. The RF frames were acquired by a Verasonics Vantage 256 system using a 5 MHz (L7-4) linear array. The acquired RF frames were beamformed using a delay and sum algorithm, and a GPU-based, 1-D cross correlation method was used to estimate the wall velocities. The acceleration peak of the pulse wave was tracked to estimate PWV. PWVs of 2.6 ms−1, 3.4 ms−1, and 4.5 ms−1 were, respectively, measured for the soft, medium, and stiff plaque material in experimental phantoms. The PWV of 2.7 m/s was confirmed for the medium plaque in simulation. This fundamental study demonstrated, thus, a good accuracy by PWI in the estimation of plaque PWV and its underlying stiffness for future stroke risk assessment.

Towards Robust Blood Pressure Estimation From Pulse Wave Velocity Measured by Photoplethysmography Sensors

In recent years, advances in biosensing devices have greatly increased the ability of sensing human biological vital signs. These advances have allowed physicians to better assess the health status of patients. Among them, blood pressure (BP) sensing has been the dominant one, showing the most potential for growth. Despite much progress, a rapid, robust, and easy-accessed way for BP sensing is still much needed for the emerging point-of-care market. To tackle this challenge, in this paper, we present a BP measurement unit that is developed based on two photoplethysmography (PPG) sensors from which pulse wave velocity (PWV) of blood flow can be derived. A robust time difference of collected two subsequent PPG waveforms between two heartbeats was used to calculate PWV. The systolic and diastolic BPs from 26 participants were estimated by using the derived PWV as the input of machine learning (ML) models. Upon testing multiple ML models, the Gaussian process regressor achieved the highest R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of >0.88 and >0.62 for the systolic BP and diastolic BP, respectively. These R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> scores are among the best that can be achieved with state-of-art non-invasive BP measurement devices. This work demonstrates that PPG-based sensors for PWV and BP estimation, combined with ML, have a great potential of becoming a complementary way to measure biological vital signs.

Acute effects of exercise on cardiac autonomic function and arterial stiffness in patients with stable coronary artery disease

Objectives. To examine the acute effect of a maximal aerobic exercise effort on aortic, peripheral arterial stiffness and cardiovagal modulation of trained and untrained patients with coronary artery disease (CAD). Design. Cross-sectional study. Methods. Eighteen untrained patients with CAD, 18 trained patients with CAD, and 18 apparently healthy trained subjects were sampled and matched for age and body mass index. Aortic and peripheral stiffness were measured by applanation tonometry estimates of carotid-femoral (cfPWV), carotid-radial (crPWV), and carotid-dorsalis pedis pulse wave velocity (cdPWV), respectively. Cardiovagal modulation was assessed by heart-rate variability (HRV) indices including the standard deviation of normal-to-normal RR intervals (SDNN), root-mean-square of successive differences (RMSSD), and the high-frequency power band (HF). cfPWV, crPWV, cdPWV, and HRV indices were measured at rest, 10 and 30 min following a maximal cardiopulmonary exercise test on a cycle ergometer. Results. No differences were observed between groups at rest nor over time in indices of HRV, cfPWV and cdPWV. Still, main effects of time were observed in cfPWV (p < .001; ɳ 2 = 0.313) and cdPWV (p = .003, ɳ 2 = 0.111), RMSSD (p < .001, ɳ 2 = 0.352), HF (p < .001, ɳ 2 = 0.265) and LF/HF (p = .001, ɳ 2 = 0.239), as cdPWV, RMSSD, and HF were reduced 10 min following exercise, whereas cfPWV and LF/HF were increased. Changes in cPP were associated with changes in HRV from rest to min 10 (HF, r = 0.302), and to min 30 (HF, r = 0.377; SDNN, r = 0.357; RMSSD, r = 0.429). Conclusion. Training level and CAD do not seem to influence arterial stiffness and cardiac autonomic responses to maximal exercise.