57 research outputs found

    An intracardiac electrogram model to bridge virtual hearts and implantable cardiac devices

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    Virtual heart models have been proposed to enhance the safety of implantable cardiac devices through closed loop validation. To communicate with a virtual heart, devices have been driven by cardiac signals at specific sites. As a result, only the action potentials of these sites are sensed. However, the real device implanted in the heart will sense a complex combination of near and far-field extracellular potential signals. Therefore many device functions, such as blanking periods and refractory periods, are designed to handle these unexpected signals. To represent these signals, we develop an intracardiac electrogram (IEGM) model as an interface between the virtual heart and the device. The model can capture not only the local excitation but also far-field signals and pacing afterpotentials. Moreover, the sensing controller can specify unipolar or bipolar electrogram (EGM) sensing configurations and introduce various oversensing and undersensing modes. The simulation results show that the model is able to reproduce clinically observed sensing problems, which significantly extends the capabilities of the virtual heart model in the context of device validation

    A reaction-diffusion heart model for the closed-loop evaluation of heart-pacemaker interaction

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    The purpose of this manuscript is to develop a reaction-diffusion heart model for closed-loop evaluation of heart-pacemaker interaction, and to provide a hardware setup for the implementation of the closed-loop system. The heart model, implemented on a workstation, is based on the cardiac monodomain formulation and a phenomenological model of cardiac cells, which we fitted to the electrophysiological properties of the different cardiac tissues. We modelled the pacemaker as a timed automaton, deployed on an Arduino 2 board. The Arduino and the workstation communicate through a PCI acquisition board. Additionally, we developed a graphical user interface for easy handling of the framework. The myocyte model resembles the electrophysiological properties of atrial and ventricular tissue. The heart model reproduces healthy activation sequence and proved to be computationally efficient (i.e., 1 s simulation requires about 5 s). Furthermore, we successfully simulated the interaction between heart and pacemaker models in three well-known pathological contexts. Our results showed that the PDE formulation is appropriate for the simulation in closed-loop. While computationally more expensive, a PDE model is more flexible and allows to represent more complex scenarios than timed or hybrid automata. Furthermore, users can interact more easily with the framework thanks to the graphical representation of the spatiotemporal evolution of the membrane potentials. By representing the heart as a reaction-diffusion model, the proposed closed-loop system provides a novel and promising framework for the assessment of cardiac pacemakers

    An audit of uncertainty in multi-scale cardiac electrophysiology models

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    Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’

    Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

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    Cardiac digital twins provide a physics and physiology informed framework to deliver predictive and personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs and the high number of model evaluations needed for patient-specific personalization. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. In this work, we use Latent Neural Ordinary Differential Equations (LNODEs) to learn the temporal pressure-volume dynamics of a heart failure patient. Our surrogate model based on LNODEs is trained from 400 3D-0D whole-heart closed-loop electromechanical simulations while accounting for 43 model parameters, describing single cell through to whole organ and cardiovascular hemodynamics. The trained LNODEs provides a compact and efficient representation of the 3D-0D model in a latent space by means of a feedforward fully-connected Artificial Neural Network that retains 3 hidden layers with 13 neurons per layer and allows for 300x real-time numerical simulations of the cardiac function on a single processor of a standard laptop. This surrogate model is employed to perform global sensitivity analysis and robust parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor. We match pressure and volume time traces unseen by the LNODEs during the training phase and we calibrate 4 to 11 model parameters while also providing their posterior distribution. This paper introduces the most advanced surrogate model of cardiac function available in the literature and opens new important venues for parameter calibration in cardiac digital twins

    Mathematical Modelling of Cardiac Rhythms in Health and Disease

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    Cardiac disease is the most common cause of death among the adult population worldwide and atrial fibrillation (AF) is the most common cardiac arrhythmia. The state of the art in AF treatment involves creating lesions of heart tissue through radiofrequency ablation. In this thesis, mathematical modelling techniques are developed to design decision support tools that could help a cardiologist determine the best location to ablate in clinic. Firstly, parameter optimisation methods are explored to adapt a model designed for the ventricles to the atria, and a novel technique is introduced to characterise pathways through parameter space from a healthy state to a diseased state using a multi-objective genetic algorithm. Next, I reproduce clinical signals recorded during AF ablation through the use of a phenomenological model of the cardiac action potential on a cylinder and show how this model can enable us to recover information lost in clinic to improve clinical decision. This is followed by introducing a more simplistic approach to the same problem, by characterising the electrical activity on the recording by a sine wave. Finally, the effectiveness of these two approaches is compared in the clinical setting by testing both as decision support tools. The emphasis of the approaches throughout the thesis is on developing techniques with clinical applicability. We demonstrate that lost information in clinic can affect the decision made by an experienced clinician, and that the mathematical modelling approaches developed in the thesis can significantly reduce the impact that this information loss can have on clinical decision making

    Modelling the interaction between induced pluripotent stem cells derived cardiomyocytes patches and the recipient hearts

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    Cardiovascular diseases are the main cause of death worldwide. The single biggest killer is represented by ischemic heart disease. Myocardial infarction causes the formation of non-conductive and non-contractile, scar-like tissue in the heart, which can hamper the heart's physiological function and cause pathologies ranging from arrhythmias to heart failure. The heart can not recover the tissue lost due to myocardial infarction due to the myocardium's limited ability to regenerate. The only available treatment is heart transpalant, which is limited by the number of donors and can elicit an adverse response from the recipients immune system. Recently, regenerative medicine has been proposed as an alternative approach to help post-myocardial infarction hearts recover their functionality. Among the various techniques, the application of cardiac patches of engineered heart tissue in combination with electroactive materials constitutes a promising technology. However, many challenges need to be faced in the development of this treatment. One of the main concerns is represented by the immature phenotype of the stem cells-derived cardiomyocytes used to fabricate the engineered heart tissue. Their electrophysiological differences with respect to the host myocardium may contribute to an increased arrhythmia risk. A large number of animal experiments are needed to optimize the patches' characteristics and to better understand the implications of the electrical interaction between patches and host myocardium. In this Thesis we leveraged cardiac computational modelling to simulate \emph{in silico} electrical propagation in scarred heart tissue in the presence of a patch of engineered heart tissue and conductive polymer engrafted at the epicardium. This work is composed by two studies. In the first study we designed a tissue model with simplified geometry and used machine learning and global sensitivity analysis techniques to identify engineered heart tissue patch design variables that are important for restoring physiological electrophysiology in the host myocardium. Additionally, we showed how engineered heart tissue properties could be tuned to restore physiological activation while reducing arrhythmic risk. In the second study we moved to more realistic geometries and we devised a way to manipulate ventricle meshes obtained from magnetic resonance images to apply \emph{in silico} engineered heart tissue epicardial patches. We then investigated how patches with different conduction velocity and action potential duration influence the host ventricle electrophysiology. Specifically, we showed that appropriately located patches can reduce the predisposition to anatomical isthmus mediated re-entry and that patches with a physiological action potential duration and higher conduction velocity were most effective in reducing this risk. We also demonstrated that patches with conduction velocity and action potential duration typical of immature stem cells-derived cardiomyocytes were associated with the onset of sustained functional re-entry in an ischemic cardiomyopathy model with a large transmural scar. Finally, we demonstrated that patches electrically coupled to host myocardium reduce the likelihood of propagation of focal ectopic impulses. This Thesis demonstrates how computational modelling can be successfully applied to the field of regenerative medicine and constitutes the first step towards the creation of patient-specific models for developing and testing patches for cardiac regeneration.Open Acces

    Computer-Aided Clinical Trials For Medical Devices

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    Life-critical medical devices require robust safety and efficacy to treat patient populations with potentially large patient heterogeneity. Today, the de facto standard for evaluating medical devices is the randomized controlled trial. However, even after years of device development many clinical trials fail. For example, in the Rhythm ID Goes Head to Head Trial (RIGHT) the risk for inappropriate therapy by implantable cardioverter defibrillators (ICDs) actually increased relative to control treatments. With recent advances in physiological modeling and devices incorporating more complex software components, population-level device outcomes can be obtained with scalable simulations. Consequently, there is a need for data-driven approaches to provide early insight prior to the trial, lowering the cost of trials using patient and device models, and quantifying the robustness of the outcome. This work presents a clinical trial modeling and statistical framework which utilizes simulation to improve the evaluation of medical device software, such as the algorithms in ICDs. First, a method for generating virtual cohorts using a physiological simulator is introduced. Next, we present our framework which combines virtual cohorts with real data to evaluate the efficacy and allows quantifying the uncertainty due to the use of simulation. Results predicting the outcome of RIGHT and improving statistical power while reducing the sample size are shown. Finally, we improve device performance with an approach using Bayesian optimization. Device performance can degrade when deployed to a general population despite success in clinical trials. Our approach improves the performance of the device with outcomes aligned with the MADIT-RIT clinical trial. This work provides a rigorous approach towards improving the development and evaluation of medical treatments
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