scholarly journals Parameter Estimation of a 3D Cardiac Electrophysiology Model Including the Restitution Curve Using Optical and MR Data

2009 ◽  
pp. 1716-1719 ◽  
Author(s):  
J. Relan ◽  
M. Sermesant ◽  
M. Pop ◽  
H. Delingette ◽  
M. Sorine ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Cardiac Electrophysiology ◽  
Restitution Curve

Markov models for ion channels: versatility versus identifiability and speed

2009 ◽  
Vol 367 (1896) ◽  
pp. 2161-2179 ◽  
Author(s):  
Martin Fink ◽  
Denis Noble
Keyword(s):  
Parameter Estimation ◽  
Cardiac Electrophysiology ◽  
Markov Models ◽  
Single Channel ◽  
Model Development ◽  
Computational Cost ◽  
Computation Time ◽  
State Variables ◽  
Gating Currents ◽  
State Dependent

Markov models (MMs) represent a generalization of Hodgkin–Huxley models. They provide a versatile structure for modelling single channel data, gating currents, state-dependent drug interaction data, exchanger and pump dynamics, etc. This paper uses examples from cardiac electrophysiology to discuss aspects related to parameter estimation. (i) Parameter unidentifiability (found in 9 out of 13 of the considered models) results in an inability to determine the correct layout of a model, contradicting the idea that model structure and parameters provide insights into underlying molecular processes. (ii) The information content of experimental voltage step clamp data is discussed, and a short but sufficient protocol for parameter estimation is presented. (iii) MMs have been associated with high computational cost (owing to their large number of state variables), presenting an obstacle for multicellular whole organ simulations as well as parameter estimation. It is shown that the stiffness of models increases computation time more than the number of states. (iv) Algorithms and software programs are provided for steady-state analysis, analytical solutions for voltage steps and numerical derivation of parameter identifiability. The results provide a new standard for ion channel modelling to further the automation of model development, the validation process and the predictive power of these models.

scholarly journals Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

2021 ◽  
Vol 12 ◽  
Author(s):  
Sam Coveney ◽  
Cesare Corrado ◽  
Jeremy E. Oakley ◽  
Richard D. Wilkinson ◽  
Steven A. Niederer ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Cardiac Electrophysiology ◽  
Probabilistic Models ◽  
Principal Component ◽  
Fundamental Aspect ◽  
Model Parameters ◽  
Bayesian Calibration ◽  
Restitution Curve ◽  
Study Parameter ◽  
Noisy Measurements

Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into cardiac function. Restitution curves provide information on tissue function and can be measured using clinically feasible measurement protocols. We introduce novel “restitution curve emulators” as probabilistic models for performing model exploration, sensitivity analysis, and Bayesian calibration to noisy data. These emulators are built by decomposing restitution curves using principal component analysis and modeling the resulting coordinates with respect to model parameters using Gaussian processes. Restitution curve emulators can be used to study parameter identifiability via sensitivity analysis of restitution curve components and rapid inference of the posterior distribution of model parameters given noisy measurements. Posterior uncertainty about parameters is critical for making predictions from calibrated models, since many parameter settings can be consistent with measured data and yet produce very different model behaviors under conditions not effectively probed by the measurement protocols. Restitution curve emulators are therefore promising probabilistic tools for calibrating electrophysiology models.

scholarly journals Spatially Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology

2017 ◽  
Vol 36 (9) ◽  
pp. 1966-1978 ◽  
Author(s):  
Jwala Dhamala ◽  
Hermenegild J. Arevalo ◽  
John Sapp ◽  
Milan Horacek ◽  
Katherine C. Wu ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Cardiac Electrophysiology ◽  
Local Parameter ◽  
Multi Scale ◽  
Spatially Adaptive ◽  
Scale Optimization

Weighted Combination of Prior and Sample Information and Parameter Estimation

Optimization ◽  
1976 ◽  
Vol 7 (5) ◽  
pp. 665-672
Author(s):  
H. Burke ◽  
C. Hennig ◽  
W H. Schmidt
Keyword(s):  
Parameter Estimation ◽  
Sample Information ◽  
Weighted Combination

Sequential accuracy in parameter estimation for population correlation coefficients.

2019 ◽  
Vol 24 (4) ◽  
pp. 492-515 ◽  
Author(s):  
Ken Kelley ◽  
Francis Bilson Darku ◽  
Bhargab Chattopadhyay
Keyword(s):  
Parameter Estimation ◽  
Correlation Coefficients ◽  
Population Correlation

Parameter Estimation for Industrial Servomotor by Using Statistical Hypothesis Test

2019 ◽  
Vol 19 (2) ◽  
pp. 134-140
Author(s):  
Baek-Ju Sung ◽  
Sung-kyu Lee ◽  
Mu-Seong Chang ◽  
Do-Sik Kim
Keyword(s):  
Parameter Estimation ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Test
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