Derivation of the ascending aortic-carotid pressure transfer function with an arterial model

1996 ◽  
Vol 271 (6) ◽  
pp. H2399-H2404 ◽  
Author(s):  
M. Karamanoglu ◽  
M. P. Feneley
Keyword(s):  
Characteristic Impedance ◽  
Aortic Pressure ◽  
Arterial System ◽  
Model Parameters ◽  
Pressure Waveform ◽  
Mean Square ◽  
Arterial Waveform ◽  
Arterial Model ◽  
Viscoelastic Tube ◽  
Carotid Arterial

To devise a method of deriving the ascending aortic pressure waveform from the noninvasively determined carotid arterial waveform, ascending aortic and carotid arterial pressures were recorded in 13 patients aged 58.5 +/- 10.0 (SD) yr. A single viscoelastic tube terminated with a modified windkessel was used to model the carotid arterial system. For each patient the model parameters, characteristic impedance of the tube (Z0), reflection coefficient at the termination (gamma), and time constant of the windkessel (tau), were estimated by minimizing the root-mean-square error between the measured and predicted carotid waveforms, with the ascending aortic pressure waveform as input. The resulting arterial parameters were realistic: Z0 = 729.5 +/- 246.8 dyn.s.cm-3, gamma = 0.75 +/- 0.19, and tau = 0.16 +/- 0.17 s. A generalized model constructed with these mean parameters yielded a smaller error between predicted and measured carotid arterial pressures (3.4 +/- 1.3 mmHg) than between ascending aortic pressure and measured carotid arterial pressure (4.4 +/- 1.6 mmHg, P < 0.01) and also reproduced the carotid wave contour indexed by the ratio of late systolic to early systolic peak amplitude: predicted = 1.26 +/- 0.05 and measured = 1.24 +/- 0.16 vs. aortic = 1.55 +/- 0.19.

Comparison of linear and nonlinear formulations of the three-element windkessel model

1996 ◽  
Vol 271 (6) ◽  
pp. H2661-H2668 ◽  
Author(s):  
R. Fogliardi ◽  
M. Di Donfrancesco ◽  
R. Burattini
Keyword(s):  
Diastolic Pressure ◽  
Estimation Error ◽  
Aortic Pressure ◽  
Arterial System ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Windkessel Model ◽  
Linear Version ◽  
Compliance Model ◽  
Better Than

The three-element windkessel model incorporating a constant compliance (model A) was compared with two nonlinear versions of the same model (models B1 and B2) incorporating a pressure-dependent compliance. The aim was to test whether nonlinear elasticity yielded better model behavior in describing ascending aortic pressure-flow relationships and interpreting the physical properties of the arterial system. Exponential and bell-shaped compliance vs. pressure curves were assumed in models B1 and B2, respectively. To test these models, we used measurements of ascending aortic pressure and flow from three dogs under a wide variety of hemodynamic states obtained by administering vasoactive drugs and by pacing the heart. These data involved pressure waves with and without an evident oscillation during diastole. Model parameters were estimated by fitting experimental and model-predicted ascending aortic pressures. Our results indicated that only models A and B1 were identifiable. Fits to ascending aortic pressure obtained from model B1 were significantly better than fits obtained from model A. However, 1) the accuracy of parameter estimates, as judged from parameter estimation error analysis, was better in model A than in model B1, 2) the estimates of characteristic parameters of the compliance vs. pressure relation in model B1 were inconsistent with expected physiological trends of this relation, and 3) model B1 did not improve the approximation of diastolic pressure in the presence of an evident oscillation. We conclude that, even in the presence of better data fit, the nonlinear three-element windkessel cannot be preferred over the traditional linear version of this model.

Response of systemic arterial input impedance to volume expansion and hemorrhage

1980 ◽  
Vol 238 (6) ◽  
pp. H902-H908 ◽  
Author(s):  
J. P. Dujardin ◽  
D. N. Stone ◽  
L. T. Paul ◽  
H. P. Pieper
Keyword(s):  
Blood Volume ◽  
Volume Expansion ◽  
Characteristic Impedance ◽  
Aortic Pressure ◽  
Arterial System ◽  
Aortic Smooth Muscle ◽  
Direct Measurements ◽  
Dextran 70 ◽  
Anesthetized Dogs ◽  
The Mean

Experiments on 12 anesthetized dogs were performed to study the effects of changes in blood volume on the pulsatile hemodynamics of the arterial system as seen from its input. Pressure and flow were measured in the ascending aorta under control conditions, after volume expansion with dextran 70 (+30% of estimated blood volume), and after hemorrhage (-15% of estimated blood volume). The input inpedance of the arterial system was calculated for each condition. It was found that after volume expansion the characteristic impedance of the proximal aorta, Zc, was decreased by 26.6 +/- 5.1% (SE) (P less than 0.01). After hemorrhage Zc was increased by 30.4 +/- 3.4% (P less than 0.01). Since it is well known that Zc is a very weak function of the mean arterial pressure, it is concluded that the changes in Zc seen with volume expansion or hemorrhage are caused mainly by changes in aortic smooth muscle activity. This conclusion is also supported by direct measurements of aortic pressure diameter relationships in earlier work from our lab.

Effects of Diabetes and Gender on Mechanical Properties of the Arterial System in Rats: Aortic Impedance Analysis1

2003 ◽  
Vol 228 (1) ◽  
pp. 70-78 ◽  
Author(s):  
Kuo-Chu Chang ◽  
Kwan-Lih Hsu ◽  
Yung-Zu Tseng
Keyword(s):  
Transit Time ◽  
Input Impedance ◽  
Wave Reflection ◽  
Characteristic Impedance ◽  
Aortic Distensibility ◽  
Aortic Pressure ◽  
Diabetic Rats ◽  
Arterial System ◽  
Reflection Factor ◽  
Aortic Input Impedance

We determined the effects of diabetes and gender on the physical properties of the vasculature in streptozotocin (STZ)-treated rats based on the aortic input impedance analysis. Rats given STZ 65 mg/kg i.v. were compared with untreated age-matched controls. Pulsatile aortic pressure and flow signals were measured and were then subjected to Fourier transformation for the analysis of aortic input impedance. Wave transit time was determined using the impulse response function of the filtered aortic input impedance spectra. Male but not female diabetic rats exhibited an increase in cardiac output in the absence of any significant changes in arterial blood pressure, resulting in a decline in total peripheral resistance. However, in each gender group, diabetes contributed to an increase in wave reflection factor, from 0.47 ± 0.04 to 0.84 ± 0.03 in males and from 0.46 ± 0.03 to 0.81 ± 0.03 in females. Diabetic rats had reduced wave transit time, at 18.82 ± 0.60 vs 21.34 ± 0.51 msec in males and at 19.63 ± 0.37 vs 22.74 ± 0.57 msec in females. Changes in wave transit time and reflection factor indicate that diabetes can modify the timing and magnitude of the wave reflection in the rat arterial system. Meanwhile, diabetes produced a fall in aortic characteristic impedance from 0.023 ± 0.002 to 0.009 ± 0.001 mmHg/min/kg/ml in males and from 0.028 ± 0.002 to 0.014 ± 0.001 mmHg/min/kg/ml in females. With unaltered aortic pressure, both the diminished aortic characteristic impedance and wave transit time suggest that the muscle inactivation in diabetes may occur in aortas and large arteries and may cause a detriment to the aortic distensibility in rats with either sex. We conclude that only rats with male gender diabetes produce a detriment to the physical properties of the resistance arterioles. In spite of male or female gender, diabetes decreases the aortic distensibility and impairs the wave reflection phenomenon in the rat arterial system.

Abstract 355: Calculating Pulse Wave Velocity from a Single Pressure Waveform Using the Intrinsic Frequency Method

Hypertension ◽  
2014 ◽  
Vol 64 (suppl_1) ◽  
Author(s):  
Niema M Pahlevan ◽  
Danny Petrasek ◽  
Derek G Rinderknecht ◽  
Peyman Tavallali ◽  
Morteza Gharib
Keyword(s):  
Pulse Wave Velocity ◽  
Wave Velocity ◽  
Pulse Wave ◽  
Aortic Pressure ◽  
Coupled Systems ◽  
Arterial System ◽  
Pressure Waveform ◽  
Intrinsic Frequency ◽  
Frequency Method ◽  
Time Frequency

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.

Abstract 16334: An Artificial Intelligence Derived Method for Instantaneous Detection of Elevated Left Ventricular End Diastolic Pressure

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Niema M Pahlevan ◽  
Rashid Alavi ◽  
Melissa Ramos ◽  
Antreas Hindoyan ◽  
Ray V Matthews
Keyword(s):  
Systems Approach ◽  
Diastolic Pressure ◽  
Medical Center ◽  
Aortic Pressure ◽  
Outcome Data ◽  
Left Ventricular ◽  
Heart Catheterization ◽  
Pressure Waveform ◽  
Arterial Waveform ◽  
Artificial Neural Network Ann

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.

Wave reflection augments central systolic and pulse pressures during facial cooling

2008 ◽  
Vol 294 (6) ◽  
pp. H2535-H2539 ◽  
Author(s):  
David G. Edwards ◽  
Matthew S. Roy ◽  
Raju Y. Prasad
Keyword(s):  
Cardiovascular Events ◽  
Wave Reflection ◽  
Augmentation Index ◽  
Systolic Pressure ◽  
Aortic Pressure ◽  
Applanation Tonometry ◽  
Pressure Waveform ◽  
Central Aortic Pressure ◽  
Facial Cooling ◽  
Change In Mean

Cardiovascular events are more common in the winter months, possibly because of hemodynamic alterations in response to cold exposure. The purpose of this study was to determine the effect of acute facial cooling on central aortic pressure, arterial stiffness, and wave reflection. Twelve healthy subjects (age 23 ± 3 yr; 6 men, 6 women) underwent supine measurements of carotid-femoral pulse wave velocity (PWV), brachial artery blood pressure, and central aortic pressure (via the synthesis of a central aortic pressure waveform by radial artery applanation tonometry and generalized transfer function) during a control trial (supine rest) and a facial cooling trial (0°C gel pack). Aortic augmentation index (AI), an index of wave reflection, was calculated from the aortic pressure waveform. Measurements were made at baseline, 2 min, and 7 min during each trial. Facial cooling increased ( P < 0.05) peripheral and central diastolic and systolic pressures. Central systolic pressure increased more than peripheral systolic pressure (22 ± 3 vs. 15 ± 2 mmHg; P < 0.05), resulting in decreased pulse pressure amplification ratio. Facial cooling resulted in a robust increase in AI and a modest increase in PWV (AI: −1.4 ± 3.8 vs. 21.2 ± 3.0 and 19.9 ± 3.6%; PWV: 5.6 ± 0.2 vs. 6.5 ± 0.3 and 6.2 ± 0.2 m/s; P < 0.05). Change in mean arterial pressure but not PWV predicted the change in AI, suggesting that facial cooling may increase AI independent of aortic PWV. Facial cooling and the resulting peripheral vasoconstriction are associated with an increase in wave reflection and augmentation of central systolic pressure, potentially explaining ischemia and cardiovascular events in the cold.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

Pulse wave analysis of the aortic pressure waveform in patients with vasovagal syncope

2014 ◽  
Vol 31 (1) ◽  
pp. 74-79 ◽  
Author(s):  
Simon Pecha ◽  
Samer Hakmi ◽  
Iris Wilke ◽  
Yalin Yildirim ◽  
Boris Hoffmann ◽  
...  
Keyword(s):  
Pulse Wave ◽  
Vasovagal Syncope ◽  
Aortic Pressure ◽  
Pulse Wave Analysis ◽  
Pressure Waveform ◽  
Wave Analysis

scholarly journals A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Lin Lin ◽  
Fang Wang ◽  
Shisheng Zhong
Keyword(s):  
Engine Performance ◽  
Grey Model ◽  
Model Parameters ◽  
Bayesian Regularization ◽  
Safety Engineering ◽  
The Novel ◽  
Mean Square ◽  
Approximation Model ◽  
Mean Square Errors ◽  
Fitting Accuracy

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

Sign in / Sign up

Export Citation Format

Share Document