Beat-to-beat sensitivity analysis of human systemic circulation coupled with the left ventricle model of the heart: A simulation-based study

Gul, Raheem; Shahzadi, Shaista

doi:10.1140/epjp/i2019-12673-3

Cited by 11 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are only two parameters: the peak H max and the minimum H min . 50 It models the heart contraction physically as a capacitor with varying capacitance. The fluid dynamic meaning could be a tank with a fluctuating base area.…”

Section: Boundary Conditions With a Lumped Modelmentioning

confidence: 99%

“…The problem can be resolved by eliminating the original elastance equation and introducing a new variable: y ¼ Δp=E. 50 Instead of solving the original elastance equation from Table 2, a new equation from Table 3 is solved, avoiding numerical instabilities. Although it keeps the conditional number low, two additional linear, algebraic equations must be solved relating the new variable y to the pressure p at the starting and ending node of the elastance element.…”

Section: Boundary Conditions With a Lumped Modelmentioning

confidence: 99%

“…Elastance function (H max ¼ 2:5 mmHg/mL, H min ¼ 0:06 mmHg/mL),50 and the visualisation of the source of numerical instability at the beginning of the cycle T A B L E 3 Modified edge and additional nodal equations for the elastance element after substituting y ¼ Δp=E…”

mentioning

confidence: 99%

See 2 more Smart Citations

First blood: An efficient, hybrid one‐ and zero‐dimensional, modular hemodynamic solver

Wéber

Gyürki

Paál

2023

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Low‐dimensional (1D or 0D) models can describe the whole human blood circulation, for example, 1D distributed parameter model for the arterial network and 0D concentrated models for the heart or other organs. This paper presents a combined 1D‐0D solver, called first_blood, that solves the governing equations of fluid dynamics to model low‐dimensional hemodynamic effects. An extended method of characteristics is applied here to solve the momentum, and mass conservation equations and the viscoelastic wall model equation, mimicking the material properties of arterial walls. The heart and the peripheral lumped models are solved with a general zero‐dimensional (0D) nonlinear solver. The model topology can be modular, that is, first_blood can solve any 1D‐0D hemodynamic model. To demonstrate the applicability of first_blood, the human arterial system, the heart and the peripherals are modelled using the solver. The simulation time of a heartbeat takes around 2 s, that is, first_blood requires only twice the real‐time for the simulation using an average PC, which highlights the computational efficiency. The source code is available on GitHub, that is, it is open source. The model parameters are based on the literature suggestions and on the validation of output data to obtain physiologically relevant results.

show abstract

Section: Boundary Conditions With a Lumped Modelmentioning

confidence: 99%

Section: Boundary Conditions With a Lumped Modelmentioning

confidence: 99%

See 1 more Smart Citation

First blood: An efficient, hybrid one‐ and zero‐dimensional, modular hemodynamic solver

Wéber

Gyürki

Paál

2023

Numer Methods Biomed Eng

View full text Add to dashboard Cite

show abstract

“…9 It is believed that using these models can help improve the prediction of cardiovascular activities, and more importantly, it provides a convincing mechanism explanation for this prediction. 10 Especially, hemodynamic mathematical modeling usually contains three different scales, which have led to three typical models respectively, including 0-dimensional (0-D) model, 11 1-dimensional (1-D) model, 12 and 3-dimensional (3-D) model. 13 Considering that 1-D model can deliver accurate information on blood flow and pressure with a low computational cost, it has been widely verified and developed, [14][15][16][17] and is also mainly discussed in this manuscript.…”

Section: Introductionmentioning

confidence: 99%

A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced‐order model

Zhou

et al. 2021

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Precise model personalization is a key step towards the application of cardiovascular physical models. In this manuscript, we propose to use deep learning (DL) to solve the parameter estimation problem in cardiovascular hemodynamics. Based on the convolutional neural network (CNN) and fully connected neural network (FCNN), a multi-input deep neural network (DNN) model is developed to map the nonlinear relationship between measurements and the parameters to be estimated. In this model, two separate network structures are designed to extract the features of two types of measurement data, including pressure waveforms and a vector composed of heart rate (HR) and pulse transit time (PTT), and a shared structure is used to extract their combined dependencies on the parameters.Besides, we try to use the transfer learning (TL) technology to further strengthen the personalized characteristics of a trained-well network. For assessing the proposed method, we conducted the parameter estimation using synthetic data and in vitro data respectively, and in the test with synthetic data, we evaluated the performance of the TL algorithm through two individuals with different characteristics. A series of estimation results show that the estimated parameters are in good agreement with the true values. Furthermore, it is also found that the estimation accuracy can be significantly improved by a multicycle combination strategy. Therefore, we think that the proposed method has the potential to be used for parameter estimation in cardiovascular hemodynamics, which can provide an immediate, accurate, and sustainable personalization process, and deserves more attention in the future.

show abstract

“…In a normalized local sensitivity analysis, the impact of a single model parameter is studied at a time on all output variables. Several researchers used this analysis in different biological engineering problems [24][25][26][27][28]. As mentioned in the previous paragraphs, that flow of GF inside the kidney is complex, and after the reabsorption of useful substances, its nature becomes thin to make the urine.…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamics and Sensitivity Analysis of a Williamson Fluid in Porous-Walled Wavy Channel

Shahzad¹,

Khan²,

Gul³

et al. 2021

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

In this work, a steady, incompressible Williamson fluid model is investigated in a porous wavy channel. This situation arises in the reabsorption of useful substances from the glomerular filtrate in the kidney. After 80% reabsorption, urine is left, which behaves like a thinning fluid. The laws of conservation of mass and momentum are used to model the physical problem. The analytical solution of the problem in terms of stream function is obtained by a regular perturbation expansion method. The asymptotic integration method for small wave amplitudes and the RK-Fehlberg method for pressure distribution has been used inside the channel. It is demonstrated that the forward flow becomes fast in the narrow region (at x = 0.75), which dominates the upward flow inside the channel. To study the impact of model parameters on outputs, we applied normalized local sensitivity analysis and noticed that the most influential parameter for the longitudinal velocity profile is the dimensionless wave amplitude. The reabsorption parameter is sensitive for transverse velocity in the narrow region, and the Weissenberg number has a strong effect on the pressure inside the channel. Further, the least sensitive parameters for the velocity components and pressure have been identified.

show abstract

Beat-to-beat sensitivity analysis of human systemic circulation coupled with the left ventricle model of the heart: A simulation-based study

Cited by 11 publications

References 19 publications

First blood: An efficient, hybrid one‐ and zero‐dimensional, modular hemodynamic solver

First blood: An efficient, hybrid one‐ and zero‐dimensional, modular hemodynamic solver

A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced‐order model

Hydrodynamics and Sensitivity Analysis of a Williamson Fluid in Porous-Walled Wavy Channel

Contact Info

Product

Resources

About