Regurgitant volume in aortic regurgitation from a parameter estimation procedure

1994 ◽  
Vol 76 (3) ◽  
pp. 1378-1383 ◽  
Author(s):  
S. A. Slordahl ◽  
H. F. Kuecherer ◽  
J. E. Solbakken ◽  
H. Piene ◽  
B. A. Angelsen ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Total Peripheral Resistance ◽  
Arterial Compliance ◽  
Peripheral Resistance ◽  
Estimation Procedure ◽  
Regurgitant Volume ◽  
Regurgitant Fraction ◽  
Orifice Area ◽  
Parameter Estimation Procedure ◽  
Regurgitant Orifice Area

The regurgitant volume and regurgitant orifice area as well as total peripheral resistance and arterial compliance were estimated in a cardiovascular hydromechanical simulator and in 10 patients with aortic regurgitation. A parameter estimation procedure based on a simple model of the cardiovascular system, Doppler measurements of the regurgitant jet, aortic systolic flow, and systolic and diastolic blood pressures was used. In the cardiovascular simulator the estimated regurgitant orifice area was compared with the size of a hole in the disk of a mechanical aortic valve. In the patients the regurgitant fraction was compared with semiquantitative grading from echocardiography routinely performed in our laboratory. In the hydromechanical simulator, the estimated regurgitant orifice area of 26.5 +/- 3.5 (SD) mm2 (n = 9) was not different from the true value of 24 mm2. In the patients there was a fair relationship between the estimated regurgitant fraction and the semiquantitative grading. The estimated regurgitant orifice areas varied between 1.6 and 31.2 mm2. The estimated mean values of total peripheral resistance and arterial compliance were 1.67 +/- 0.55 mmHg.s.ml-1 and 1.30 +/- 0.42 ml/mmHg, respectively.

Pulmonary arterial compliance at rest and exercise in normal humans

1990 ◽  
Vol 258 (6) ◽  
pp. H1823-H1828 ◽  
Author(s):  
D. M. Slife ◽  
R. D. Latham ◽  
P. Sipkema ◽  
N. Westerhof
Keyword(s):  
Parameter Estimation ◽  
Pulmonary Artery ◽  
Arterial Compliance ◽  
Estimation Procedure ◽  
Windkessel Model ◽  
Parameter Estimation Procedure ◽  
Significant Difference ◽  
Normal Humans ◽  
Pulmonary Arterial ◽  
Rest And Exercise

We evaluated the feasibility of determining pulmonary arterial compliance (Cp) by a parameter estimation procedure based on the three-element windkessel model. Eight normal patients studied with multisensor micromanometry technology had simultaneous rest and exercise pulmonary artery pressures (PAP) and flows recorded. These were submitted to the model and independent methods to determine Cp, pulmonary characteristic impedance (Zc), and pulmonary vascular resistance (PVR). Significant changes in heart rate, PAP, and stroke volume (P less than 0.05) occurred with exercise. In comparing rest and exercise Zc and PVR values determined by the model and independent methods, and in comparing each method for these values, there was no significant difference. Model-derived and independently derived estimates of Cp were significantly different at rest (P less than 0.04) and exercise (P less than 0.001). There was no significant difference between rest and exercise values of Cp by either method. The model estimates of PVR at rest (64 +/- 11 dyn.s.cm-5) and exercise (41 +/- 7 dyn.s.cm-5) (P = 0.06) and the model Zc value at rest (22 +/- 3 dyn.s.cm5) were appropriate. The model Cp values at rest (0.22 +/- 0.05 ml.mmHg-1.kg-1) correlated with previously reported normalized values in other species. This study reports the successful use of a parameter estimation procedure based on the three-element windkessel model to describe pulmonary artery compliance in normal humans.

THE REALIZATION OF THE ITERATIVE METHOD OF THE LEAST SQUARES FOR THE ESTIMATION OF STATIC OBJECT PARAMETERS IN MATLAB ENVIRONMENT

2017 ◽  
pp. 28-36
Author(s):  
Galina Vasil’evna Troshina ◽  
Alexander Aleksandrovich Voevoda
Keyword(s):  
Parameter Estimation ◽  
Iterative Method ◽  
Least Squares ◽  
Input Signal ◽  
Layer Structure ◽  
Estimation Procedure ◽  
Parameter Estimation Procedure ◽  
Noise Simulation ◽  
Static Object ◽  
Object Parameters

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.

scholarly journals A Simple Methodology to Estimate the Diffusion Coefficient in Pervaporation-Based Purification Experiments

Polymers ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 343 ◽  
Author(s):  
Gabriela Dudek ◽  
Przemysław Borys
Keyword(s):  
Diffusion Coefficient ◽  
Parameter Estimation ◽  
Series Solution ◽  
Time Course ◽  
Estimation Procedure ◽  
Feed Solution ◽  
Time Lags ◽  
Parameter Estimation Procedure ◽  
Chitosan Membranes ◽  
Hydrophilic Membranes

A procedure to estimate the diffusion coefficient in solution–diffusion models of hydrophilic membranes used in pervaporation-based purification experiments is presented. The model is based on a series solution of the general permeation problem. It considers a membrane that can be filled with water or with the feed solution before the measurement. Furthermore, the length of the tubing between the permeation cell and the place of cold traps is also addressed. To illustrate the parameter estimation procedure, we have chosen the data for the separation of water and ethanol by chitosan membranes. It is shown that the diffusion coefficient can be estimated effectively from the time course of the transported mass and by the analysis of certain well defined time lags of the permeation curve.

scholarly journals Model calibration using the automatic parameter estimation procedure (PEST) of the North-eastern zone of the Milan Functional Urban Area (Italy)

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Luca Alberti
Keyword(s):  
Parameter Estimation ◽  
Urban Area ◽  
Model Calibration ◽  
Estimation Procedure ◽  
The North ◽  
Parameter Estimation Procedure ◽  
Eastern Zone ◽  
North Eastern

La Functional Urban Area (FUA) di Milano è un’area densamente popolata (2.254.263 abitanti) dove l’approvvigionamento idrico è garantito esclusivamente mediante prelievi idrici sotterranei. Per questa ragione la protezione della qualità delle falde rientra tra le priorità delle politiche ambientali di Regione Lombardia. Recentemente è stato avviato un programma di studi ed interventi aventi lo scopo d’individuare i principali plumes di contaminazione da solventi clorurati distinguendone l’impatto da quello legato all’inquinamento diffuso. In questo articolo si presenta il modello di flusso sviluppato per il settore NE della FUA di Milano, settore utilizzato quale area pilota per sviluppare e testare una nuova metodologia che combina statistica e modellistica al fine di distinguere il contributo delle fonti di contaminazione puntuale rispetto a quelle diffuse.

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