Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

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

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 Review on Software Architectures for Heterogeneous Platforms

The increasing demands for computing performance have been a reality regardless of the requirements for smaller and more energy efficient devices. Throughout the years, the strategy adopted by industry was to increase the robustness of a single processor by increasing its clock frequency and mounting more transistors so more calculations could be executed. However, it is known that the physical limits of such processors are being reached, and one way to fulfill such increasing computing demands has been to adopt a strategy based on heterogeneous computing, i.e., using a heterogeneous platform containing more than one type of processor. This way, different types of tasks can be executed by processors that are specialized in them. Heterogeneous computing, however, poses a number of challenges to software engineering, especially in the architecture and deployment phases. In this paper, we conduct an empirical study that aims at discovering the state-of-the-art in software architecture for heterogeneous computing, with focus on deployment. We conduct a systematic mapping study that retrieved 28 studies, which were critically assessed to obtain an overview of the research field. We identified gaps and trends that can be used by both researchers and practitioners as guides to further investigate the topic

Validating the Comparison Framework for the Finite Dimensions Model of Concentric Ring Electrodes Using Human Electrocardiogram Data

[EN] While progress has been made in design optimization of concentric ring electrodes maximizing the accuracy of the surface Laplacian estimation, it was based exclusively on the negligible dimensions model of the electrode. Recent proof of concept of the new finite dimensions model that adds the radius of the central disc and the widths of concentric rings to the previously included number of rings and inter-ring distances provides an opportunity for more comprehensive design optimization. In this study, the aforementioned proof of concept was developed into a framework allowing direct comparison of any two concentric ring electrodes of the same size and with the same number of rings. The proposed framework is illustrated on constant and linearly increasing inter-ring distances tripolar concentric ring electrode configurations and validated on electrocardiograms from 20 human volunteers. In particular, ratios of truncation term coefficients between the two electrode configurations were used to demonstrate the similarity between the negligible and the finite dimension models analytically (p = 0.077). Laplacian estimates based on the two models were calculated on electrocardiogram data for emulation of linearly increasing inter-ring distances tripolar concentric ring electrode. The difference between the estimates was not statistically significant (p >> 0.05) which is consistent with the analytic result.This research was funded by the National Science Foundation (NSF) Division of Human Resource Development (HRD) Tribal Colleges and Universities Program (TCUP), grants number 1622481 and 1914787 to Oleksandr Makeyev. The authors would like to thank Rafael Rodriguez de Sanabria for his help with the human ECG data collection and Eduardo Garcia-Breijo for his help with the CRE implementation.Makeyev, O.; Musngi, M.; Moore, L.; Ye Lin, Y.; Prats-Boluda, G.; Garcia-Casado, J. 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A reaction-diffusion heart model for the closed-loop evaluation of heart-pacemaker interaction

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

Transferring Generalized Knowledge from Physics-based Simulation to Clinical Domain

A primary factor for the success of machine learning is the quality of labeled training data. However, in many fields, labeled data can be costly, difficult, or even impossible to acquire. In comparison, computer simulation data can now be generated at a much higher abundance with a much lower cost. These simulation data could potentially solve the problem of data deficiency in many machine learning tasks. Nevertheless, due to model assumptions, simplifications and possible errors, there is always a discrepancy between simulated and real data. This discrepancy needs to be addressed when transferring the knowledge from simulation to real data. Furthermore, simulation data is always tied to specific settings of models parameters, many of which have a considerable range of variations yet not necessarily relevant to the machine learning task of interest. The knowledge extracted from simulation data must thus be generalizable across these parameter variations before being transferred. In this dissertation, we address the two outlined challenges in leveraging simulation data to overcome the shortage of labeled real data, . We do so in a clinical task of localizing the origin of ventricular activation from 12 lead electrocardiograms (ECGs), where the clinical ECG data with labeled sites of origin in the heart can only be invasively available. By adopting the concept of domain adaptation, we address the discrepancy between simulated and clinical ECG data by learning the shift between the two domains using a large amount of simulation data and a small amount of clinical data. By adopting the concept of domain generalization, we then address the reliance of simulated ECG data on patient-specific geometrical models by learning to generalize simulated ECG data across subjects, before transferring them to clinical data. Evaluated on in-vivo premature ventricular contraction (PVC) patients, we demonstrate the feasibility of utilizing a large number of offline simulated ECG datasets to enable the prediction of the origin of arrhythmia with only a small number of clinical ECG data on a new patient

Fibroblast mediated dynamics in diffusively uncoupled myocytes -- a simulation study using 2-cell motifs

In healthy hearts myocytes are typically coupled to nearest neighbours through gap junctions. Under pathological conditions such as fibrosis, or in scar tissue, or across ablation lines myocytes can uncouple from their neighbours. Electrical conduction may still occur via fibroblasts that not only couple proximal myocytes but can also couple otherwise unconnected regions. We hypothesise that such coupling can alter conduction between myocytes via introduction of delays or by initiation of premature stimuli that can potentially result in reentry or conduction blocks. To test this hypothesis we have developed several $2$-cell motifs and investigated the effect of fibroblast mediated electrical coupling between uncoupled myocytes. We have identified various regimes of myocyte behaviour that depend on the strength of gap-junctional conductance, connection topology, and parameters of the myocyte and fibroblast models. These motifs are useful in developing a mechanistic understanding of long-distance coupling on myocyte dynamics and enable the characterisation of interaction between different features such as myocyte and fibroblast properties, coupling strengths and pacing period. They are computationally inexpensive and allow for incorporation of spatial effects such as conduction velocity. They provide a framework for constructing scar tissue boundaries and enable linking of cellular level interactions with scar induced arrhythmia

Transgenic rabbit models for cardiac disease research.

To study the pathophysiology of human cardiac diseases and to develop novel treatment strategies, complex interactions of cardiac cells on cellular, tissue and whole heart levels need to be considered. As in vitro cell-based models do not depict the complexity of the human heart, animal models are used to obtain insights that can be translated to human diseases. Mice are the most commonly used animals in cardiac research, however, differences in electrophysiological and mechanical cardiac function and a different composition of electrical and contractile proteins limit the transferability of the knowledge gained. Moreover, the small heart size and fast heart rate are major disadvantages. In contrast to rodents, electrophysiological, mechanical, and structural cardiac characteristics of rabbits resemble the human heart more closely, making them particularly suitable as an animal model for cardiac disease research. In this review, various methodological approaches for the generation of transgenic rabbits for cardiac disease research - such as pronuclear microinjection, the sleeping beauty transposon system and novel genome editing methods (ZFN and CRISPR/Cas9) - will be discussed. In the second section, we will introduce the different currently available transgenic rabbit models for monogenic cardiac diseases (such as long-QT syndrome, short-QT syndrome, and hypertrophic cardiomyopathy) in detail, especially in regards to their utility to increase the understanding of pathophysiological disease-mechanisms and novel treatment options