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Intraventricular hemorrhage is characterized by blood leaking into the cerebral ventricles and mixing with cerebrospinal fluid. A standard treatment method involves inserting a passive drainage catheter, known as an external ventricular drain (EVD), into the ventricle. EVDs have common adverse complications, including the occlusion of the catheter, that may lead to permanent neural damage or even mortality. In order to prevent such complications, a novel dual-lumen catheter (IRRAflow®) utilizing an active fluid exchange mechanism has been recently developed. However, the fluid dynamics of the exchange system have not been investigated. In this study, convective flow in a three-dimensional cerebral lateral ventricle with an inserted catheter is evaluated using an in-house lattice-Boltzmann-based fluid–solid interaction solver. Different treatment conditions are simulated, including injection temperature and patient position. Thermal and gravitational effects on medication distribution are studied using a dye simulator based on a recently-introduced (pseudo)spectral convection–diffusion equation solver. The effects of injection temperature and patient position on catheter performance are presented and discussed in terms of hematoma irrigation, vortical structures, mixing, and medication volume distribution. Results suggest that cold-temperature injections can increase catheter efficacy in terms of dye distribution and irrigation potential, both of which can be further guided by patient positioning.

Introduction: Previous studies demonstrated that heat failure with preserved ejection fraction (HFpEF) is characterized by abnormal arterial function and left ventricle (LV)-arterial coupling. Aortic characteristic impedance (Zc) and total arterial compliance (TAC) are well-established biomarkers for quantification of the arterial function. However, the routine clinical use of these biomarkers requires both pressure and flow measurements. Intrinsic Frequency (IF) method is a novel system-based mathematical approach for the analysis of the cardiovascular dynamics that can predict HF events (Cooper et al. Hypertension, 2021 PMID: 33390053). We hypothesized that a hybrid IF-machine learning (ML) approach can compute TAC and Zc from a noninvasive carotid waveform. Method: We used data from the Framingham Heart Study (FHS) (n=6168). The reference values of Zc and TAC were computed from carotid pressure and aortic flow waveforms. IF parameters of carotid pressure waveform were used in a fully connected feed-forward neural network model. The model was trained/validated/tested on 80% of randomly selected data from all FHS data. The final model was tested on the remaining 20% of the data blinded to all stages of ML development. Result: In blind test set data, our IF-ML models showed correlations of r=0.89 and r=0.79 with the reference value of TAC and Zc, respectively (Fig 1). More importantly, the TAC and Zc from our IF-ML models showed strong correlations among HFpEF patients (r=0.9 and r=0.77 respectively) (Fig.1) Conclusion: Since IFs can be measured noninvasively (e.g. an iPhone), this method can be used to facilitate routine and noninvasive evaluation of TAC and Zc in HFpEF.

For univentricular heart patients, the Fontan operation creates a unique non-physiologic circulation where the pulmonary arteries are directly supplied from the systemic venous blood return, leading to chronic non-pulsatile low-shear-rate pulmonary blood flow. Computational fluid dynamic (CFD) simulations are widely used in cardiovascular biofluid studies and a Newtonian fluid assumption is common since blood acts as a Newtonian fluid in large arteries. Although blood viscosity increases exponentially in low-shear-rate flow environments, a Newtonian assumption is still conventionally used in both experimental and CFD studies of the low-shear-rate Fontan circulation. The error introduced by a Newtonian assumption in this setting has not been thoroughly evaluated. Recent in vitro studies have demonstrated that non-Newtonian effects are substantial in the Fontan circulation. However, no previous studies have used CFD to systemically investigate non-Newtonian effects in Fontan simulations. Thus the role of shear-rate-dependent non-Newtonian blood viscosity in this pathophysiology is still unclear. In this study we used a computational approach to compare flow behavior between a non-Newtonian fluid and a Newtonian fluid in a simplified model of the Fontan circulation over a wide range of experimental conditions. We examined important clinical metrics in Fontan hemodynamics: shear stress, power loss, pulmonary flow distribution, viscous dissipation, and non-Newtonian importance factors. Our results suggest that the Newtonian fluid assumption for blood introduces considerable error into the simulations of the Fontan circulation, where the fluid environment is predominated by low-shear-rate flow.

In-vitro models of the systemic circulation have gained a lot of interest for fundamental understanding of cardiovascular dynamics and for applied hemodynamic research. In this study, we introduce a physiologically accurate in-vitro hydraulic setup that models the hemodynamics of the coupled atrioventricular-aortic system. This unique experimental simulator has three major components: 1) an arterial system consisting of a human-scale artificial aorta along with the main branches, 2) an artificial left ventricle (LV) sac connected to a programmable piston-in-cylinder pump for simulating cardiac contraction and relaxation, and 3) an artificial left atrium (LA). The setup is designed in such a way that the basal LV is directly connected to the aortic root via an aortic valve, and to the LA via an artificial mitral valve. As a result, two-way hemodynamic couplings can be achieved for studying the effects that the LV, aorta, and LA have on each other. The collected pressure and flow measurements from this setup demonstrate a remarkable correspondence to clinical hemodynamics. We also investigate the physiological relevancies of isolated effects on cardiovascular hemodynamics of various major global parameters found in the circulatory system, including LV contractility, LV preload, heart rate, aortic compliance, and peripheral resistance. Subsequent control over such parameters ultimately captures physiological hemodynamic effects of LV systolic dysfunction, preload (cardiac) diseases, and afterload (arterial) diseases. The detailed design and fabrication of the proposed setup is also provided.

Analysis of cardiovascular waveforms provides valuable clinical information about the state of health and disease. The intrinsic frequency (IF) method is a recently introduced framework that uses a single arterial pressure waveform to extract physiologically relevant information about the cardiovascular system. The clinical usefulness and physiological accuracy of the IF method have been well-established via several preclinical and clinical studies. However, the computational complexity of the current L2 optimization solver for IF calculations remains a bottleneck for practical deployment of the IF method in real-time settings. In this paper, we propose a machine learning (ML)-based methodology for determination of IF parameters from a single carotid waveform. We use a sequentially-reduced Feedforward Neural Network (FNN) model for mapping carotid waveforms to the output parameters of the IF method, thereby avoiding the non-convex L2 minimization problem arising from the conventional IF approach. Our methodology also includes procedures for data pre-processing, model training, and model evaluation. In our model development, we used both clinical and synthetic waveforms. Our clinical database is composed of carotid waveforms from two different sources: the Huntington Medical Research Institutes (HMRI) iPhone Heart Study and the Framingham Heart Study (FHS). In the HMRI and FHS clinical studies, various device platforms such as piezoelectric tonometry, optical tonometry (Vivio), and an iPhone camera were used to measure arterial waveforms. Our blind clinical test shows very strong correlations between IF parameters computed from the FNN-based method and those computed from the standard L2 optimization-based method (i.e., R≥0.93 and P-value ≤0.005 for each IF parameter). Our results also demonstrate that the performance of the FNN-based IF model introduced in this work is independent of measurement apparatus and of device sampling rate.

Thoracic endovascular aortic repair (TEVAR) is a commonly performed operation for patients with type B aortic dissection (TBAD). The goal of TEVAR is to cover the proximal entry tear between the true lumen (TL) and the false lumen (FL) with an endograft to induce FL thrombosis, allow for aortic healing, and decrease the risk of aortic aneurysm and rupture. While TEVAR has shown promising outcomes, it can also result in devastating complications including stroke, spinal cord ischemia resulting in paralysis, as well as long-term heart failure, so treatment remains controversial. Similarly, the biomechanical impact of aortic endograft implantation and the hemodynamic impact of endograft design parameters such as length are not well-understood. In this study, a fluid-structure interaction (FSI) computational fluid dynamics (CFD) approach was used based on the immersed boundary and Lattice–Boltzmann method to investigate the association between the endograft length and hemodynamic variables inside the TL and FL. The physiological accuracy of the model was evaluated by comparing simulation results with the true pressure waveform measurements taken during a live TEVAR operation for TBAD. The results demonstrate a non-linear trend towards increased FL flow reversal as the endograft length increases but also increased left ventricular pulsatile workload. These findings suggest a medium-length endograft may be optimal by achieving FL flow reversal and thus FL thrombosis, while minimizing the extra load on the left ventricle. These results also verify that a reduction in heart rate with medical therapy contributes favorably to FL flow reversal.

Introduction: Non-invasive and instantaneous evaluation of left ventricular end-diastolic pressure (LVEDP) is of high significance in diagnosis and treatment of heart failure patients. We recently proposed the cardiac triangle mapping (CTM), a systems approach that uses information from arterial pressure waveforms and pre-ejection periods to map the global ventricular function (Pahlevan et al. Fluids 4.1 (2019): 16). Here, we propose a hybrid machine learning (ML) method based on CTM theory that uses pressure waveforms at the iliac bifurcation level and ECG to instantaneously classify the level of LVEDP. Methods: We studied 46 patients (Age: 39-90 (66.4±9.9), BMI: 20.2-36.8 (27.6±4.1), 12 females) who were scheduled for clinical left heart catheterization or coronary angiogram at the Keck Medical Center of USC. Exclusion criteria were valvular heart disease, left bundle branch block, or atrial fibrillation. Invasive LVEDP and pressure waveforms at iliac bifurcation were measured using a 3F Millar transducer tipped catheter, with simultaneous 3 channel ECG. The range of patients’ LVEDP was from 9.3 to 40.5 mmHg. LVEDP=18 mmHg was used as the ML classification cut-off. A random forest classifier (of n=30 grown decision trees, ensemble learning method: bootstrap aggregation, 6 inputs) was trained using data from 36 patients. The model was blindly tested on 10 additional patients. Results: Our ML model showed accuracies of 100.0% and 80.0% for predicting the true class of LVEDP on blind test data (i.e., elevated or normal, respectively) (Fig1). The accuracy on all data was higher than 93.4% for all classes. Conclusions: We demonstrated the proof-of-concept that a physics-based ML model can instantaneously classify the LV filling pressure using information extracted from femoral waveform and ECG. Our study is invasive validation; however, all required ML input parameters can be obtained noninvasively and remotely (i.e., using wearable devices or a smartphone).

Introduction: Left ventricle (LV) pressure-volume loop (LVPVL) is an informative manifestation of LV mechanical dynamics. Seemann et al. (Circulation: Cardiovascular Imaging. 2019;12(1): e008493) proposed a novel non-invasive LVPVL approximation method using brachial blood pressure and cardiac MRI with the double-Hill model characterizing the time-varying LV elastance. However, their clinical validation was performed on a small population with a significant age difference between the control and HF groups. Methods: We evaluated the noninvasive LVPVL method on the iPhone-HMRI study cohort that includes non-HF (N = 102) and HF (N =25) participants. (Pahlevan et al. Critical Care Medicine ,2017 PMID: 28441235) Mean[range] of age (years) were 53[20-92] and 55[26-88] for the non-HF and the HF groups, respectively. Stroke work, potential energy, external power, energy per ejected volume, contractility, and LV efficiency from the estimated loops were compared between the non-HF and HF groups. Results: The estimated LVPVLs for HF group yielded significantly higher mechanical potential energy and energy per ejected volume, but significantly lower ventricular efficiency and contractility (p Conclusions: Our results with an age-matching control group agree with the previously reported results. However, since this method uses a fixed guesses of LV end-diastolic pressure, it is suspected that the resultant estimated LVPVL cannot effectively characterize the variation along the pressure axis and the loop-area for features such as stroke work and mean external power. Improvements are needed to leverage hemodynamic information in arterial pressure waveforms (i.e., carotid) to enhance the sensitivity of such non-invasive LVPVL determination based on time-varying LV elastance model and arterial waveforms measured using tonometry.

The characterization of transient flows within the Reynolds number range of Re = 10–100 and the Womersley number range α = 0.8–10 is required for the ongoing development of green monopropellant thrusters. However, at the ml/min scale flow rates of interest, these measurements are outside the capabilities of current commercial flow meters. It is proposed here that transient flows under the required dynamic conditions can be characterized via Womersley's solution for transient flow in a rigid tube. This solution method requires only the measured transient pressure gradient within a controlled laminar flow section and can be accomplished using existing commercial pressure measurement hardware. Experiments were performed where flow similarity was maintained with the ultimate thruster characterization application, but the radius of the flow passage was increased so that flows could be simultaneously characterized by both the proposed solution method and a commercial ultrasonic flow meter. It was shown that across the range of interest, applying Womersley's solution to a measured pressure gradient was an effective method of transient flow characterization. Additionally, it was shown that non-periodic flows can be characterized except for the initial flow startup transient with solution convergence times approximating the analytical solution to starting flow in a pipe. While these results were expected due to an experimental design matching the assumptions required for Womersley's analytic solution, this work demonstrates that this method is practically feasible as novel instrumentation enabling previously unobtainable measurements.

Background.Wave intensity (WI) analysis is a well-established method for quantifying the energy carried in arterial waves, providing valuable clinical information about cardiovascular function. The primary drawback of this method is the need for concurrent measurements of both pressure and flow waveforms.Objective. We have for the first time investigated the accuracy of a novel methodology for estimating wave intensity employing only single pressure waveform measurements; we studied both carotid- and radial-based estimations in a large heterogeneous cohort.Approach.Tonometry was performed alongside Doppler ultrasound to acquire measurements of both carotid and radial pressure waveforms as well as aortic flow waveforms in 2640 healthy and diseased participants (1439 female) in the Framingham Heart Study. Patterns consisting of two forward waves (Wf1, Wf2) and one backward wave (Wb1) along with reflection metrics were compared with those obtained from exact WI analysis.Main Results. Carotid-based estimates correlated well for forward peak amplitudes (Wf1,r = 0.85,p < 0.05; Wf2,r = 0.72,p < 0.05) and peak time (Wf1,r = 0.94,p < 0.05; Wf2,r = 0.98,p < 0.05), and radial-based estimates correlated fairly to poorly for amplitudes (Wf1,r = 0.62,p < 0.05; Wf2,r = 0.42,p < 0.05) and peak time (Wf1,r = 0.04,p = 0.10; Wf2,r = 0.75,p < 0.05). In all cases, estimated Wb1 measures were not correlated. Reflection metrics were well correlated for healthy patients (r = 0.67,p < 0.05), moderately correlated for valvular disease (r = 0.59,p < 0.05) and fairly correlated for CVD (r = 0.46,p < 0.05) and heart failure (r = 0.49,p < 0.05).Significance. These findings indicate that pressure-only WI produces accurate results only when forward contributions are of primary interest and only for carotid pressure waveforms. The pressure-only WI estimations of this work provide an important opportunity to further the goal of uncovering clinical insights through wave analysis affordably and non-invasively.

Radial applanation tonometry is a well-established method for clinical hemodynamic assessment and is also becoming popular in wrist-worn fitness trackers. The time difference between the foot and the dicrotic notch of the arterial pressure waveform is a well-accepted approximation for the left ventricular ejection time (ET). However, several clinical studies have shown that ET measured from the radial pressure waveform deviates from that measured centrally. In this work, we consider the systolic wave and the dicrotic wave as two independent traveling waves and hypothesize that their wave speed difference leads to the intersite differences of measured ET (ΔET). Accordingly, we derived a mathematical dicrotic wave decomposition model and identified the most influential factors on ΔET via global sensitivity analysis. In our clinical validation on a heterogeneous cohort (N = 5,742) from the Framingham Heart Study (FHS), the local sensitivity analysis results resembled the sensitivity variation patterns of ΔET from model simulations. A regression analysis on FHS data, using morphological features of radial pressure waveforms to estimate the carotid ET, produced a root mean square error of 3.76 ms and R2 of 0.91. The proposed dicrotic wave decomposition model can explain the intersite ET measurement discrepancies observed in the clinical data of FHS and can facilitate the precise identification of ET with radial pressure waveforms. Therefore, the proposed model will improve various physics-based pulse wave analysis methods as well as prospective artificial intelligence methods for tackling the subsequent big data produced from widespread wearable radial pressure monitoring.NEW & NOTEWORTHY Based on a new understanding of pressure wave propagation, we propose a novel dicrotic wave decomposition model considering the dicrotic wave as an independent traveling component. The proposed model can explain the mechanism underlying the intersite discrepancies in ejection time measurement from arterial waveforms and then, in principle, enhance the accuracy of both classical physics-based as well as more contemporary artificial intelligence-based pulse wave analysis methods in clinical and wearable radial blood pressure monitoring applications.

Introduction: Transcatheter aortic valve replacement (TAVR) is an established treatment for patients with severe, symptomatic aortic stenosis (AS). Ventriculoarterial (LV-arterial) coupling defined as the ratio of total arterial elastance (Ea) to left ventricular end-systolic elastance (Ees) reflects effective cardiac energetics and is a well-accepted index for quantification of LV-arterial coupling. Despite its usefulness, estimating Ees/Ea has technical difficulties. Intrinsic Frequency (IF) method is a noninvasive and single waveform system-based approach for quantification of LV-arterial coupling. The objective of this study was to compare IF variables with Ea/Ees in predicting optimum LV-arterial energetics following TAVR. Method: Twenty-eight patients with severe AS, undergoing TAVR were included. Mean age was 85±4, 53% male with mean ejection fraction 59±6.4. IFs during systole (ω1), diastole (ω2), and total IF variation (Δω=ω1-ω2) were computed from the ascending aortic pressure waveforms at baseline and following TAVR. Ea/Ees was computed using single-beat technique proposed by Takeuchi et al. ( Circulation . 1991;83(1):202-212). Results: There was a significant decrease in Ea/Ees (p Conclusion: IF appears to be an accurate and reliable index for quantification of LV-arterial coupling given significant concordance with Ea/Ees. The management of patients with acutely altered hemodynamic states post TAVR can benefit from the assessment of LV-arterial coupling. Since IFs can be measured noninvasively using hand-held devices (e.g. an iPhone), this approach should broaden the clinical applicability of this useful parameter for assessing systolic function, therapeutic response and ventricular-arterial interaction post TAVR.

Introduction: Instantaneous, non-invasive detection of an elevated left ventricular end-diastolic pressure (LVEDP) offers a significant benefit in diagnosis and treatment of heart failure. We recently proposed a systems approach, called cardiac triangle mapping (CTM), that uses intrinsic frequencies (IFs) of the arterial waveform and pre-ejection period (PEP) to map the global ventricular function (Pahlevan et al. Fluids 4.1 (2019): 16). Here, we tested the hypothesis that an elevated LVEDP can be detected using ECG and arterial pressure waveform by applying an artificial neural network (ANN) combined with CTM approach. Methods: This study included 46 patients (12 females, age 39-90 (66.4±9.9), BMI 20.2-36.8 (27.6±4.1)) who were scheduled for a clinical left heart catheterization or coronary angiogram at the Keck Medical Center of USC. Exclusion criteria were valvular heart disease, atrial fibrillation, or left bundle branch block. Invasive LVEDP and aortic pressure waveforms were measured using a 3F Millar transducer tipped catheter with simultaneous 3 channel ECG. The IFs were computed from pressure waveforms. PEPs were calculated as the time difference between the beginning of QRS and the uprising of the pressure waveform. A 3-layer network consisted of 6 input, 6 hidden and one output nodes was developed. LVEDP=18 mmHg was used as the cut-off for a binary outcome. Data from 34 patients were used to design the ANN (27 for training, 7 for validation). The model was tested on 12 additional patients. Results: Our results showed a specificity of 87% and a sensitivity of 96% in detecting an elevated LVEDP (Fig.1). Conclusions: Here, we demonstrated the proof-of-concept that an AI model based on reduced-order parameters (extracted from arterial waveform and ECG) can instantaneously detect an elevated LVEDP. Although our hemodynamic measurements were done invasively, all variables that are required for this AI-LVEDP calculation can be collected noninvasively.

Introduction: Intrinsic Frequency (IF) method is a recently developed systems-based method that extracts dynamics information about left ventricle function (LV), arterial dynamics, and the LV-arterial coupling from arterial waveforms. We have recently shown (Alavi et al. Circulation, 140 (2019), A12573-A12573) that IF can detect occurrence of an acute myocardial infarction (MI) using a single carotid pressure waveform. Here, we propose that the myocardial infarct size (area of necrosis over total LV area) can be approximated using a hybrid IF-artificial neural network (ANN) method. Methods: The standard MI model was used in anesthetized Sprague Dawley rats (n=27). The proximal left coronary artery was occluded for 30 minutes to ensure necrosis followed by 3 hours of reperfusion. The left ventricle slices were incubated in triphenyl tetrazolium chloride (TTC) to distinguish the necrotic (white) and the non-necrotic (dark red) areas (Fig.1a), thereby obtaining the size of MI through histopathology. IF parameters were computed from random carotid pressure waveforms 2 hours after the reperfusion. A 3-layer ANN model (4 input, 5 hidden, and 1 output node) was applied on IFs from 22 rats to design the ANN (18 for training, 4 for validation). The model was then tested on 5 different rats with the same MI procedure described above. Results: The results showed a significant correlation (R=0.64, P Conclusions: Our results suggest that a hybrid IF-AI method can predict the anatomic infarct size from an arterial waveform without advanced imaging. This technique is clinically significant since infarct sizes are link to the survival and development of heart failure in MI patients, and IF parameters can be obtained noninvasively from carotid waveforms using arterial tonometry devices or an iPhone.

Our group thought the study by Lee and Kim entitled “Hemodynamic Changes in the Carotid Artery after Infusion of Normal Saline Using Computational Fluid Dynamics” was a very elegant method to discern the changes in blood rheology within the carotid sinus after administration of crystalloid [...]

Purpose: Childhood cancer survivors are at risk for anthracycline-related cardiac dysfunction, often developing at a time when they are least engaged in long-term survivorship care. New paradigms in survivorship care and chronic disease screening are needed in this population. We compared the accuracy of a novel handheld mHealth platform (Vivio) as well as echocardiography for assessment of cardiac function [left ventricular ejection fraction (EF)] in childhood cancer survivors with cardiac magnetic resonance (CMR) imaging (reference). Experimental Design: Cross-sectional study design was used. Concurrent evaluation of EF was performed using Vivio, two-dimensional (2D) echocardiography, and CMR. Differences in mean EF (2D echocardiography vs. CMR; Vivio vs. CMR) were compared using Bland–Altman plots. Linear regression was used to evaluate proportional bias. Results: A total of 191 consecutive survivors participated [50.7% female; median time from diagnosis: 15.8 years (2–44); median anthracycline dose: 225 mg/m2 (25–642)]. Echocardiography overestimated mean EF by 4.9% (P < 0.001); linear regression analysis confirmed a proportional bias, when compared with CMR (t = 3.1, P < 0.001). There was no difference between mean EF derived from Vivio and from CMR (–0.2%, P = 0.68). The detection of cardiac dysfunction via echocardiography was poor when compared with CMR [Echo EF < 45% (sensitivity 14.3%), Echo EF < 50% (sensitivity 28.6%)]. Sensitivity was substantially better for Vivio-based measurements [EF < 45% or EF < 50% (sensitivity 85.7%)]. Conclusions: This accessible technology has the potential to change the day-to-day practice of clinicians caring for the large number of patients diagnosed with cardiac dysfunction and heart failure each year, allowing real-time monitoring and management of their disease without the lag-time between imaging and interpretation of results. Clin Cancer Res; 24(13); 3119–25. ©2018 AACR.

The Fontan procedure for univentricular heart defects creates a unique circulation where all pulmonary blood flow is passively supplied directly from systemic veins. Computational simulations, aimed at optimizing the surgery, have assumed blood to be a Newtonian fluid without evaluating the potential error introduced by this assumption. We compared flow behavior between a non-Newtonian blood analog (0.04% xanthan gum) and a control Newtonian fluid (45% glycerol) in a simplified model of the Fontan circulation. Particle image velocimetry was used to examine flow behavior at two different cardiac outputs and two caval blood flow distributions. Pressure and flow rates were measured at each inlet and outlet. Velocity, shear strain, and shear stress maps were derived from velocity data. Power loss was calculated from pressure, flow, and velocity data. Power loss was increased in all test conditions with xanthan gum vs. glycerol (mean 10±2.9% vs. 5.6±1.3%, p=0.032). Pulmonary blood flow distribution differed in all conditions, more so at low cardiac output. Caval blood flow mixing patterns and shear stress were also qualitatively different between the solutions in all conditions. We conclude that assuming blood to be a Newtonian fluid introduces considerable error into simulations of the Fontan circulation, where low-shear flow predominates.

Intrinsic Frequency (IF) is a systems-based approach that provides valuable information for hemodynamic monitoring of the left ventricle (LV), the arterial system, and their coupling. Recent clinical studies have demonstrated the clinical significance of this method for prognosis and diagnosis of cardiovascular diseases. In IF analysis, two dominant instantaneous frequencies ( ω 1 and ω 2 ) are extracted from arterial pressure waveforms. The value of ω 1 is related to the dynamics of the LV and the value of ω 2 is related to the dynamics of vascular function. This work investigates the effects of vessel wall mechanics on the accuracy and applicability of IFs extracted from vessel wall displacement waveforms compared to IFs extracted from pressure waveforms. In this study, we used a computational approach employing a fluid–structure interaction finite element method for various wall mechanics governed by linearly elastic, hyperelastic, and viscoelastic models. Results show that for vessels with elastic wall behavior, the error between displacement-based and pressure-based IFs is negligible. In the presence of stenosis or aneurysm in elastic arteries, the maximum errors associated with displacement-based IFs is less than 2%. For non-linear elastic and viscoelastic arteries, errors are more pronounced (where the former reaches up to 11% and the latter up to 27%). Our results ultimately suggest that displacement-based computations of ω 1 and ω 2 are accurate in vessels that exhibit elastic behavior (such as carotid arteries) and are suitable surrogates for pressure-based IFs. This is clinically significant because displacement-based IFs can be measured non-invasively.

Aims Cardiovascular intrinsic frequencies (IFs) are associated with cardiovascular health and disease, separately capturing the systolic and diastolic information contained in a single (uncalibrated) arterial waveform. Previous clinical investigations related to IF have been restricted to studying chronic conditions, and hence its applicability for acute cardiovascular diseases has not been explored. Studies of cardiovascular complications such as acute myocardial infarction are difficult to perform in humans due to the high-risk and invasive nature of such procedures. Although they can be performed in preclinical (animal) models, the corresponding interpretation of IF measures and how they ultimately translate to humans is unknown. Hence, we studied the scalability of IF across species and sensor platforms. Materials and methods Scaled values of the two intrinsic frequencies ω1 and ω2 (corresponding to systolic and diastolic dynamics, respectively) were extracted from carotid waveforms acquired either non-invasively (via tonometry, Vivio or iPhone) in humans or invasively in rabbits and rats. Key findings The scaled IF parameters for all species were found to fall within the same physiological ranges carrying similar statistical characteristics, even though body sizes and corresponding heart rates of the species were substantially different. Additionally, results demonstrated that all non-invasive sensor platforms were significantly correlated with each other for scaled IFs, suggesting that such analysis is device-agnostic and can be applied to upcoming wearable technologies. Significance Ultimately, our results found that IFs are scalable across species, which is particularly valuable for the training of IF-based artificial intelligence systems using both preclinical and clinical data.

Objective: The study is based on previously reported mathematical analysis of arterial waveform that extracts hidden oscillations in the waveform that we called intrinsic frequencies. The goal of this clinical study was to compare the accuracy of left ventricular ejection fraction derived from intrinsic frequencies noninvasively versus left ventricular ejection fraction obtained with cardiac MRI, the most accurate method for left ventricular ejection fraction measurement. Design: After informed consent, in one visit, subjects underwent cardiac MRI examination and noninvasive capture of a carotid waveform using an iPhone camera (The waveform is captured using a custom app that constructs the waveform from skin displacement images during the cardiac cycle.). The waveform was analyzed using intrinsic frequency algorithm. Setting: Outpatient MRI facility. Subjects: Adults able to undergo MRI were referred by local physicians or self-referred in response to local advertisement and included patients with heart failure with reduced ejection fraction diagnosed by a cardiologist. Interventions: Standard cardiac MRI sequences were used, with periodic breath holding for image stabilization. To minimize motion artifact, the iPhone camera was held in a cradle over the carotid artery during iPhone measurements. Measurements and Main Results: Regardless of neck morphology, carotid waveforms were captured in all subjects, within seconds to minutes. Seventy-two patients were studied, ranging in age from 20 to 92 years old. The main endpoint of analysis was left ventricular ejection fraction; overall, the correlation between ejection fraction-iPhone and ejection fraction-MRI was 0.74 (r = 0.74; p < 0.0001; ejection fraction-MRI = 0.93 × [ejection fraction-iPhone] + 1.9). Conclusions: Analysis of carotid waveforms using intrinsic frequency methods can be used to document left ventricular ejection fraction with accuracy comparable with that of MRI. The measurements require no training to perform or interpret, no calibration, and can be repeated at the bedside to generate almost continuous analysis of left ventricular ejection fraction without arterial cannulation.

Objective: Here we introduced, Vivio, a wireless optical tonometer and compared its pulse waveform measurement capabilities to the AtCor SphygmoCor. Methods: Pulse waveform data from both devices was captured on a heterogeneous cohort of 29 participants (15 males, 14 females) including 16 healthy individuals free from cardiovascular diseases and 13 patients with one or more cardiovascular diseases. These participants were a subgroup of the Huntington Medical Research Institutes (HMRI) iPhone Heart Study. Results: Carotid pulse waveforms captured by Vivio were comparable to those captured using the AtCor SphygmoCor tonometer. The point-by-point root-mean-square (RMSE) error between averaged waveforms captured by either device on the same participant was 6.3% with a standard deviation of 2%. The harmonic content of the pulse waveforms captured by either device was also similar. Data collected by Vivio displayed good to excellent agreement across a wide range of pulse waveform features such as left ventricular ejection time (LVET), reflected wave arrival time (RWO), the ratio of systolic to diastolic time intervals (SDR), and heart rate (HR). A comparison between Augmentation Index (AI) measured by Vivio and tonometry showed a correlation of 0.82 (p < 0.001). Bland-Altman analysis of this data yielded a bias of -2.5% and limits of agreement spanning -15.1% to 10.1%. Significance: The Vivio has the potential to enhance the availability of noninvasive pulse waveform data thereby expanding the tools available for the diagnosis and monitoring of cardiovascular disease. When used in combination with advanced waveform analysis methods, Vivio can impact mobile healthcare resulting in improved patient health, quality of life and the overall reduction of healthcare costs.

MRI assessment of aortic pulse wave velocity (PWV) helps predict the risk of vascular events, but the recommended phase contrast sampling rate is faster than what is utilized in most clinical sequences. There are many existing MRI databases obtained for assessment of cardiac output using lower temporal frequency sampling where information might be obtained about aortic stiffness (PWV). In this work, we sought to evaluate whether the Group Delay (GD) method can generate a reproducible measure of stiffness and describe expected age-related stiffening of the aortic arch using lower sampling rates in standard clinical sequences.Phase contrast (PC) MRI was obtained on the ascending and descending aortic arch in a heterogeneous adult cohort (n = 23; 9 women) spanning over a wide range of ages (ages 24-89, mean 49.4 ± 18.4). Data was collected with standard cardiac MRI protocols for cardiac output evaluation (repetition time = 7.8 ms, views-per-segment = 4, encoding velocity = 200 cm/s). Pulse wave transit times (TT) were computed using the GD method, two other validated automated approaches (cross correlation TT Algorithm by Gaddum and Segment by Medviso), and the manual tangent method. Pressure waveforms from tonometry and flow waveforms from PC MRI were used to assess wave reflections.Group Delay and TT-Algorithm showed significant and high retest reproducibility (r = 0.86 for both) as well as high PWV correlation with age (r = 0.93, P-value 0.00005 and r = 0.96, P-value 0.00005 respectively) and with each other (r = 0.94, P-value 0.00001, RMSE = 0.94 m/s). Arbitrary altering of the image acquisition trigger in the GD method introduced error of 10%-13%, but the TT-algorithm error range was 11%-25%.Group Delay enables reproducible assessment of transit time to derive PWV from low temporal resolution clinical cardiac MRI sequences that can also identify age-related stiffening.

Recent studies showed that wave intensity analysis (WIA) provides clinically valuable information about local and global cardiovascular function. Wave intensity (WI) is computed as the product of the pressure change and the velocity change during short time intervals. The major limitation of WIA in clinical practice is the need for invasive pressure measurement. Since vessel wall displacement can be measured non-invasively, the usage of WI will be expanded if the vessel wall dilation is used instead of pressure in derivation of WI waveform. Our goal in this study is to investigate the agreement between wall displacement-based WI and the pressure-based WI for different vessel wall models including linear elastic, nonlinear and viscoelastic cases. The arbitrary Eulerian Lagrangian finite element method is employed to solve the coupled fluid-structure interaction (FSI). Our computational models also include two types of vascular disease-related cases with geometrical irregularities, aneurysm and stenosis. Our results show that for vessels with linear elastic wall, the displacement-based WI is almost identical to the pressure-based WI. The existence of vessel irregularities does not impact the accuracy of displacement-based WI. However, in a viscoelastic wall where there is a phase difference between pressure and vessel wall dilation, displacement-based WI deviated from pressure-based WI. The error associated with this phase difference increased nonlinearly with increasing viscosity. This results in a maximum error of 6.8% and 7.13% for a regular viscoelastic vessel wall and an irregular viscoelastic vessel wall, respectively. A separate analysis has also been performed on the agreement of backward and forward running waves extracted from a decomposition of the displacement-based and pressure-based WI. Our findings suggest that displacement-based WI is a reliable method of WIA for large central arteries that do not show viscoelastic behaviors. This can be clinically significant since the required information can be measured non-invasively.

The Fontan procedure for univentricular heart defects creates a nonphysiologic circulation where systemic venous blood drains directly into the pulmonary arteries, leading to multiorgan dysfunction secondary to chronic low-shear nonpulsatile pulmonary blood flow and central venous hypertension. Although blood viscosity increases exponentially in this low-shear environment, the role of shear-dependent (“non-Newtonian”) blood viscosity in this pathophysiology is unclear. We studied three-dimensional (3D)-printed Fontan models in an in vitro flow loop with a Philips 3-T magnetic resonance imaging (MRI) scanner. A 4D flow phase-contrast sequence was used to acquire a time-varying 3D velocity field for each experimental condition. On the basis of blood viscosity of a cohort of patients who had undergone the Fontan procedure, it was decided to use 0.04% xanthan gum as a non-Newtonian blood analog; 45% glycerol was used as a Newtonian control fluid. MRI data were analyzed using GTFlow and MATLAB software. The primary outcome, power loss, was significantly higher with the Newtonian fluid [14.8 (13.3, 16.4) vs. 8.1 (6.4, 9.8)%, medians with 95% confidence interval, P < 0.0001]. The Newtonian fluid also demonstrated marginally higher right pulmonary artery flow, marginally lower shear stress, and a trend toward higher caval flow mixing. Outcomes were modulated by Fontan model complexity, cardiac output, and caval flow ratio. Vortexes, helical flow, and stagnant flow were more prevalent with the non-Newtonian fluid. Our data demonstrate that shear-dependent viscosity significantly alters qualitative flow patterns, power loss, pulmonary flow distribution, shear stress, and caval flow mixing in synthetic models of the Fontan circulation. Potential clinical implications include effects on exercise capacity, ventilation-perfusion matching, risk of pulmonary arteriovenous malformations, and risk of thromboembolism. NEW & NOTEWORTHY Although blood viscosity increases exponentially in low-shear environments, the role of shear-dependent (“non-Newtonian”) blood viscosity in the pathophysiology of the low-shear Fontan circulation is unclear. We demonstrate that shear-dependent viscosity significantly alters qualitative flow patterns, power loss, pulmonary flow distribution, shear stress, and caval flow mixing in synthetic models of the Fontan circulation. Potential clinical implications include effects on exercise capacity, ventilation-perfusion matching, risk of pulmonary arteriovenous malformations, and risk of thromboembolism.

Aortic stiffness increases with age and is a robust predictor of brain pathology including Alzheimer’s and other dementias. Aging causes disproportionate stiffening of the aorta compared with the carotid arteries, reducing protective impedance mismatches at their interface and affecting transmission of destructive pulsatile energy to the cerebral circulation. Recent clinical studies have measured regional stiffness within the aortic arch using pulse wave velocity (PWV) and have found a stronger association with cerebrovascular events than global stiffness measures. However, effects of aortic arch PWV on the transmission of harmful excessive pulsatile energy to the brain is not well-understood. In this study, we use an energy-based analysis of hemodynamic waves to quantify the effect of aortic arch stiffening on transmitted pulsatility to cerebral vasculature, employing a computational approach using a one-dimensional model of the human vascular network. Results show there exists an optimum wave condition—occurring near normal human heart rates—that minimizes pulsatile energy transmission to the brain. This indicates the important role of aortic arch biomechanics on heart-brain coupling. Our results also suggest that energy-based indices of pulsatility combining pressure and flow data are more sensitive to increased stiffness than using flow or pressure pulsatility indices in isolation.

BACKGROUND: Aortic stenosis (AS) is the most common cause for valve replacement in the United States. The pathophysiology of AS involves obstruction to the left ventricular (LV) outflow and reduced arterial compliance. The intrinsic frequency (IF) method is a system-based approach for hemodynamic monitoring of the LV-arterial system and involves determination of ω1and ω2, which represent the dynamics of LV systolic and vascular function, respectively. Total frequency variation of the systemic circulation is the difference between these IFs (Δω = ω1- ω2). OBJECTIVE: Our goal in this study was to investigate whether Δω, obtained from the ascending aortic pressure waveform, can be indicative of LV-arterial coupling after transcatheter aortic valve replacement (TAVR). APPROACH: Thirty patients undergoing elective TAVR for severe, symptomatic AS were included. We applied the IF method to assess the immediate effects of TAVR on LV-arterial coupling. MAIN RESULTS: Mean age was 86 ±4 years, 50% were male with a mean aortic valve area of 0.7 cm2and mean ejection fraction (EF) of 59 ± 7%. The results showed a significant decrease in Δω (47.6 to 9.5 bpm, p < 0.00001) and a significant increase in ω2(51.9 to 84.6 bpm, p

Insulin resistance is the hallmark of classical type II diabetes. In addition, insulin resistance plays a central role in metabolic syndrome, which astonishingly affects 1 out of 3 adults in North America. The insulin resistance state can precede the manifestation of diabetes and hypertension by years. Insulin resistance is correlated with a low-grade inflammatory condition, thought to be induced by obesity as well as other conditions. Currently, the methods to measure and monitor insulin resistance, such as the homeostatic model assessment and the euglycemic insulin clamp, can be impractical, expensive, and invasive. Abundant evidence exists that relates increased pulse pressure, pulse wave velocity (PWV), and vascular dysfunction with insulin resistance. We introduce a potential method of assessing insulin resistance that relies on a novel signal-processing algorithm, the intrinsic frequency method (IFM). The method requires a single pulse pressure wave, thus the term “ wave biopsy.”

In this article, we offer an artificial intelligence method to estimate the carotid-femoral Pulse Wave Velocity (PWV) non-invasively from one uncalibrated carotid waveform measured by tonometry and few routine clinical variables. Since the signal processing inputs to this machine learning algorithm are sensor agnostic, the presented method can accompany any medical instrument that provides a calibrated or uncalibrated carotid pressure waveform. Our results show that, for an unseen hold back test set population in the age range of 20 to 69, our model can estimate PWV with a Root-Mean-Square Error (RMSE) of 1.12 m/sec compared to the reference method. The results convey the fact that this model is a reliable surrogate of PWV. Our study also showed that estimated PWV was significantly associated with an increased risk of CVDs.

Noninvasive and practical assessment of hemodynamics is a critical unmet need in the treatment of both chronic and acute cardiovascular diseases. Particularly, the ability to monitor left ventricular end-diastolic pressure (LVEDP) noninvasively offers enormous benefit for managing patients with chronic congestive heart failure. Recently, we provided proof of concept that a new cardiac metric, intrinsic frequency (IF), derived from mathematical analysis of non-invasively captured arterial waveforms, can be used to accurately compute cardiovascular hemodynamic measures, such as left ventricle ejection fraction (LVEF), by using a smartphone. In this manuscript, we propose a new systems-based method called cardiac triangle mapping (CTM) for hemodynamics evaluation of the left ventricle. This method is based on intrinsic frequency (IF) and systolic time interval (STI) methods that allows computation of LVEDP from noninvasive measurements. Since the CTM method only requires arterial waveform and electrocardiogram (ECG), it can eventually be adopted as a simple smartphone-based device, an inexpensive hand-held device, or perhaps (with future design modifications) a wearable sensor. Such devices, combined with this method, would allow for remote monitoring of heart failure patients.

In this paper, we analyse the convergence, accuracy and stability of the intrinsic frequency (IF) method. The IF method is a descendant of the sparse time frequency representation methods. These methods are designed for analysing nonlinear and non-stationary signals. Specifically, the IF method is created to address the cardiovascular system that by nature is a nonlinear and non-stationary dynamical system. The IF method is capable of handling specific nonlinear and non-stationary signals with less mathematical regularity. In previous works, we showed the clinical importance of the IF method. There, we showed that the IF method can be used to evaluate cardiovascular performance. In this article, we will present further details of the mathematical background of the IF method by discussing the convergence and the accuracy of the method with and without noise. It will be shown that the waveform fit extracted from the signal is accurate even in the presence of noise.

© 2015, Biomedical Engineering Society. The shape and formation of transmitral vortex ring are shown to be associated with diastolic function of the left ventricle (LV). Transmitral vortex ring is a flow feature that is observed to be non-axisymmetric in a healthy heart and its inherent asymmetry in the LV assists in efficient ejection of the blood during systole. This study is a first step towards understanding the effects of the mitral valve’s anterior leaflet on transmitral flow. We experimentally study a single-leaflet model of the mitral valve to investigate the effect of the anterior leaflet on the axisymmetry of the generated vortex ring based on the three-dimensional data acquired using defocusing digital particle image velocimetry. Vortex rings form downstream of a D-shaped orifice in presence or absence of the anterior leaflet in various physiological stroke ratios. The results of the statistical analysis indicate that the formed vortex ring downstream of a D-shaped orifice is markedly non-axisymmetric, and presence of the anterior leaflet improves the ring’s axisymmetry. This study suggests that the improvement of axisymmetry in presence of the anterior leaflet might be due to coupled dynamic interaction between rolling-up of the shear layer at the edges of the D-shaped orifice and the borders of the anterior leaflet. This interaction can reduce the non-uniformity in vorticity generation, which results in more axisymmetric behavior compared to the D-shaped orifice without the anterior leaflet.

The non-Newtonian behavior of blood (i.e. the dependence of viscosity on shear rate and shear stress) is most apparent in low-shear regions such as veins. For simplification, computational simulations aimed at optimizing the Fontan procedure have assumed blood to be a Newtonian fluid with shear

The reductionist approach has dominated the fields of biology and medicine for nearly a century. Here, we present a systems science approach to the analysis of physiological waveforms in the context of a specific case, cardiovascular physiology. Our goal in this study is to introduce a methodology that allows for novel insight into cardiovascular physiology and to show proof of concept for a new index for the evaluation of the cardiovascular system through pressure wave analysis. This methodology uses a modified version of sparse time–frequency representation (STFR) to extract two dominant frequencies we refer to as intrinsic frequencies (IFs; ω 1 and ω 2 ). The IFs are the dominant frequencies of the instantaneous frequency of the coupled heart + aorta system before the closure of the aortic valve and the decoupled aorta after valve closure. In this study, we extract the IFs from a series of aortic pressure waves obtained from both clinical data and a computational model. Our results demonstrate that at the heart rate at which the left ventricular pulsatile workload is minimized the two IFs are equal ( ω 1 = ω 2 ). Extracted IFs from clinical data indicate that at young ages the total frequency variation (Δ ω = ω 1 − ω 2 ) is close to zero and that Δ ω increases with age or disease (e.g. heart failure and hypertension). While the focus of this paper is the cardiovascular system, this approach can easily be extended to other physiological systems or any biological signal.

INTRODUCTION: Increased aortic stiffness is correlated with many clinically adverse cardiovascular outcomes. The “gold standard” quantitative index for arterial stiffness is the pulse wave velocity (PWV). We have developed a new method called the Intrinsic Frequency ( IF ), which views the arterial pressure waveform as a piecewise combination of two coupled systems, the heart and arterial system which are decoupled upon closure of the aortic valve. Each of these dynamical systems has an inherent frequency of operation (ω 1 and ω 2 ) which gives information about LV function (ω 1 ) as well as arterial dynamics (ω 2 ). METHODS: IF methodology is based on Sparse Time-Frequency Representation method. It uses an effective L 2 -minimization to extract the second intrinsic frequency (ω 2 ) from an aortic pressure waveform. To examine the clinical relevance of this method, IF was applied to aortic pressure waveforms taken from published works. These aortic waveforms were selected from a healthy population free of any cardiovascular diseases (CVD). RESULTS: Our results show that ω 2 represents information about the arterial system and that these measurements are highly correlated with PWV ( r=0.9 ). CONCLUSION: These results show ω 2 can potentially be used to evaluate aortic rigidity and calculate aortic PWV from one pressure waveform. Increased aortic rigidity is a common feature in normal aging and is accelerated in many CVDs including diabetes. One unique advantage of the method is that only a single measurement of the pressure waveform is required to extract the result. Therefore, ω 2 may be employed as a clinically effective noninvasive assessment of cardiovascular health.