In this paper, we present a unified electrodynamic heart model that permits simulations of the body surface potentials generated by the heart in motion. The inclusion of motion in the heart model significantly improves the accuracy of the simulated body surface potentials and therefore also the 12-lead ECG.
The key step is to construct an electromechanical heart model. The cardiac excitation propagation is simulated by an electrical heart model, and the resulting cardiac active forces are used to calculate the ventricular wall motion based on a mechanical model. The source-field point relative position changes during heart systole and diastole. These can be obtained, and then used to calculate body surface ECG based on the electrical heart-torso model.
An electromechanical biventricular heart model is constructed and a standard 12-lead ECG is simulated. Compared with a simulated ECG based on the static electrical heart model, the simulated ECG based on the dynamic heart model is more accordant with a clinically recorded ECG, especially for the ST segment and T wave of a V1-V6 lead ECG. For slight-degree myocardial ischemia ECG simulation, the ST segment and T wave changes can be observed from the simulated ECG based on a dynamic heart model, while the ST segment and T wave of simulated ECG based on a static heart model is almost unchanged when compared with a normal ECG.
This study confirms the importance of the mechanical factor in the ECG simulation. The dynamic heart model could provide more accurate ECG simulation, especially for myocardial ischemia or infarction simulation, since the main ECG changes occur at the ST segment and T wave, which correspond with cardiac systole and diastole phases.
"In terms of the weak coupling method proposed by Kerckhoffs et al. (2006), Xia et al. (2005) proposed an electromechanical biventricular model to analyze ventricular wall motion. The corresponding changes in the electrocardiogram (ECG) were simulated by Xia et al. (2006) and in the magnetocardiogram (MCG) by Shou et al. (2011). In general, the strong coupling assumption is more consistent with experimental studies, but has decreased numerical stability and increased computational load. "
[Show abstract][Hide abstract] ABSTRACT: In this study, the effects of cardiac fibroblast proliferation on cardiac electric excitation conduction and mechanical contraction were investigated using a proposed integrated myocardial-fibroblastic electromechanical model. At the cellular level, models of the human ventricular myocyte and fibroblast were modified to incorporate a model of cardiac mechanical contraction and cooperativity mechanisms. Cellular electromechanical coupling was realized with a calcium buffer. At the tissue level, electrical excitation conduction was coupled to an elastic mechanics model in which the finite difference method (FDM) was used to solve electrical excitation equations, and the finite element method (FEM) was used to solve mechanics equations. The electromechanical properties of the proposed integrated model were investigated in one or two dimensions under normal and ischemic pathological conditions. Fibroblast proliferation slowed wave propagation, induced a conduction block, decreased strains in the fibroblast proliferous tissue, and increased dispersions in depolarization, repolarization, and action potential duration (APD). It also distorted the wave-front, leading to the initiation and maintenance of re-entry, and resulted in a sustained contraction in the proliferous areas. This study demonstrated the important role that fibroblast proliferation plays in modulating cardiac electromechanical behaviour and which should be considered in planning future heart-modeling studies.
Journal of Zhejiang University SCIENCE B 03/2014; 15(3):225-42. DOI:10.1631/jzus.B1300156 · 1.28 Impact Factor
"it is large. The electrodynamic biventricular model has been validated and used to analyze the ventricular movement and mechanics , . In the ECG/MCG simulation based on the electrodynamic biventricular model, the dipole source based formulation was applied (see Section II-B). "
[Show abstract][Hide abstract] ABSTRACT: The heart movement affects the body surface electrocardiogram (ECG) and magnetocardiography (MCG). However, in the previous MCG simulation studies, the heart was always assumed static and the heart movement was seldom taken into account. In this paper, we present a simulation study of MCG based on an electrodynamic heart model to investigate the effect of heart movement on MCG. The electrodynamic biventricular model was constructed based on an electrical heart model by a weak electric-mechanic coupling. From the electrodynamic heart model, the deformation of the heart and the relationship of the dipole source and the cardiac electromagnetic field were obtained. The different performance of the MCG and ECG caused by the effects of heart movement and volume conductor model were investigated and compared. The simulation results demonstrated that the including of the heart movement will improve the accuracy of both the simulated ECG and MCG, especially for the ST interval, and the effect of heart movement on MCG is lager than that of on ECG. The volume conductor, however, has larger effect on simulated ECG/MCG of the static heart model based than that of the dynamic heart model based. This study suggested that the heart movement is more important for MCG than ECG, and should be considered in future MCG simulation study.
"The simulation protocol was based on a geometrically realistic heart-torso model    as shown in Fig. 1(a) which depicted the epicardial, lung and torso geometries and corresponding mesh information, where: the epicardial model: 187 nodes and 346 triangles ; lung model: 297 nodes and 586 triangles; torso model: 412 nodes and 820 triangles. The transfer matrix A was obtained by the Boundary Element Method (BEM). "
[Show abstract][Hide abstract] ABSTRACT: Regularization is an effective method for the solution of ill-posed ECG inverse problems, such as computing epicardial potentials from body surface potentials. The aim of this work was to explore more robust regularization-based solutions through the application of subspace preconditioned LSQR (SP-LSQR) to the study of model-based ECG inverse problems. Here, we presented three different subspace splitting methods, i.e., SVD, wavelet transform and cosine transform schemes, to the design of the preconditioners for ill-posed problems, and to evaluate the performance of algorithms using a realistic heart-torso model simulation protocol. The results demonstrated that when compared with the LSQR, LSQR-Tik and Tik-LSQR method, the SP-LSQR produced higher efficiency and reconstructed more accurate epcicardial potential distributions. Amongst the three applied subspace splitting schemes, the SVD-based preconditioner yielded the best convergence rate and outperformed the other two in seeking the inverse solutions. Moreover, when optimized by the genetic algorithms (GA), the performances of SP-LSQR method were enhanced. The results from this investigation suggested that the SP-LSQR was a useful regularization technique for cardiac inverse problems.
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