On the Role of Ionic Modeling on the Signature of Cardiac Arrhythmias for Healthy and Diseased Hearts

Ramírez, William A.; Gizzi, Alessio; Sack, K; Filippi, Sandra; Guccione, Julius M.; Hurtado, Daniel E.

doi:10.3390/math8122242

Cited by 13 publications

(6 citation statements)

References 77 publications

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“…These regions were reflected in the region of estimated abnormal tissue excitability; however, the estimated parameter values were not able to distinguish between the gray zone and dense infarct. In addition to identifiability issues associated with the presented method and the available data, this performance may also arise from the fact that the AP model considered has limited ability in differentiating electrical behavior from gray zone and infarct core (Raḿırez et al, 2020 ).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

et al. 2021

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Probabilistic estimation of cardiac electrophysiological model parameters serves an important step toward model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes direct Markov Chain Monte Carlo (MCMC) sampling of the posterior probability density function (pdf) of model parameters computationally intensive. Approximated posterior pdfs resulting from replacing the simulation model with a computationally efficient surrogate, on the other hand, have seen limited accuracy. In this study, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh. We further introduce new acquisition functions to focus the selection of training points on better approximating the shape rather than the modes of the posterior pdf of interest. We evaluated the presented method in estimating tissue excitability in a 3D cardiac electrophysiological model in a range of synthetic and real-data experiments. We demonstrated its improved accuracy in approximating the posterior pdf compared to Bayesian active learning using regular acquisition functions, and substantially reduced computational cost in comparison to existing standard or accelerated MCMC sampling.

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Section: Experiments and Resultsmentioning

confidence: 99%

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

et al. 2021

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“…Our research analysis is carried out in a single group with different channel arrhythmic conditions. Sample size was calculated by using previous study results (Ramírez et al 2020), in Clinicalc.com by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 50 for each analysis.…”

Section: Methodsmentioning

confidence: 99%

Computational Model based Approach to Analyse Calcium (Ca2+) Channel in Ventricular Cells for Normal and Cardiac Arrhythmias Using Euler Integration Method – A Simulation Study

Babu¹,

Gulothungan²

2021

alinteri

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Aim: In this paper, analysis of ventricular arrhythmias are made with respect to the Calcium (Ca2+) ion channel dysfunction (generating improper electrical activity). Many cases can make arrhythmias and most of them are related to generation or conduction of Action Potential (AP) in cardiac myocardium. Materials and method: Human ventricular cell based on the model of the human endocardial cell by Ten Tusscher (TT). The TT model data is modified based on the experimental data of Han, describing the properties of Ca2+ currents and its channel dynamics in human ventricular cells. Euler integration method is used to analyse the human ventricular model for different channel failure conditions in the same group of 50 samples. Results: Our research findings focus with respect to normal and deviant Ca2+ conductance (GCaL). The normal GCaL 0.000175nS and deviant GCaL increase like (10%=0.000218nS, 25%=0.000182nS, 50%=0.000262nS and 100%=0.000350nS) having the normal AP average value ranges between 26.0mV to -74.0mV and 12.0mV to -88.0mV for 10% GCaL, 18.0mV to -78.0mV for 25% GCaL, 18.0mV to -78.0mV for 50% GCaL and 21.0mV to -75.0mV for 100% GCaL deviants. Similarly, deviant GCaL decrease like (10%=0.000158nS, 25%=0.000131nS, 50%=0.000088nS and 100%=0.000001nS) having the deviant AP mean values ranges between 10.0mV to -90.0mV for 10% GCaL, 7.0mV to -92.0mV for 25% GCaL, -9.0mV to -96.0mV for 50% GCaL and -51.0mV to 100.0mV for 100% GCaL. Simultaneously its membrane Ca2+ currents are having significant variations. Conclusion: The results show clearly for the affirmation for Excitation and Coupling (EC) failures. EC failures lead to a systole phase that is more prolonged, that in turns to produce QT syndrome and hypertrophic cardiomyopathy.

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“…All these mathematical models possibly have assisted to improve our understanding of the analysis of the active contraction of the human cardiomyocyte [ [61] , [62] , [63] , [64] , [65] , [66] , [67] , [68] ] [ [61] , [62] , [63] , [64] , [65] , [66] , [67] , [68] ] [ [61] , [62] , [63] , [64] , [65] , [66] , [67] , [68] ].…”

Section: Background Of Myocyte Active Contractionmentioning

confidence: 99%

Mathematical modeling of active contraction of the human cardiac myocyte: A review

Asiri,

Haque Siddiqui,

Ali

et al. 2023

Heliyon

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On the Role of Ionic Modeling on the Signature of Cardiac Arrhythmias for Healthy and Diseased Hearts

Cited by 13 publications

References 77 publications

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

Computational Model based Approach to Analyse Calcium (Ca2+) Channel in Ventricular Cells for Normal and Cardiac Arrhythmias Using Euler Integration Method – A Simulation Study

Mathematical modeling of active contraction of the human cardiac myocyte: A review

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