The Visible Parallel Computational Model Of Cardioelectrical Activity

Answering how normal and various abnormal ECGs generate from the electrical activities of cardiac cells and investigating how various arrhythmias form and sustain have been questions of great significance to the diagnosis of heart diseases. Restricted by the limited means of clinical and experimental investigation, quite a few of unclear and debatable questions in cardioelectricity still exist and modeling and simulation has been valued as an important research approach. So far, most cardioelectrical models have been built based on the classical Miller-Geselowitz model that employs a dipole to represent the electrical activity of each abstract cardiac cell. Other kinds of models include the electrophysiological models and the models employing the Fitzhugh-Nagumo equation to investigate the dynamic behavior of arrhythmia. The reported electrophysiological models are one- or two-dimensional multicellular models, aiming at investigating the intercellular connection and communication, but lacking an imbedded ECG computing algorithm to link the cardioelectrical activity with the recordings of ECG. The massive parallel computing has been the main obstacle to develop a whole-heart electrophysiological model. Difficulties come from two aspects: the efficiency of executing and the convenience of describing thousands to millions groups of nonlinear action potential equations of cardiac cells. As a computation mode with intrinsic parallel features, cellular automata have received increasing attentions in recent years as the tool of discrete dynamic system simulation. Although the standard defined cellular automata are rule-based, they can be implemented as languages, with much flexibility in computation describing. As the basis of our work, we made extensions to both the language compiler and the viewing facility of Celltilae3.O, a language-based cellular automata system, and made it applicable to describe the massive parallel numerical computing. To investigate the ECG generation and the arrhythmia formation problems quantitatively and visibly at cellular and subcellular level, taking the extended cellular3.O as tool, we designed and implemented a whole-heart electrophysiological model. The two key components of this model are the cellular automata style massive parallel computing and the action potential models of cardiac cells. The included cardiac tissues are sinoatiial node, atrioventricular node, atrium, ventricle, intra-atrial conduction bundle, and intra-ventricular conduction bundle (Purkinje fiber). Restricted by the power of computer we equip now, the current model is two-dimensional now and consists of about more than four thousand cardiac cells. According to the fact that the walls of ventricles are layered and cells atND different layer have different electrical properties, we developed a resolution- nd geometry- independent run-time make-layer algorithm to layer the was of ventricles, linking the electrical property of cardiac ll with its layer position. The excitation propagation among cells of same layer is end-to-end conduction along longitudinal direction and the propagation among cells of different layer is side-to-side conduction along radial direction. Twenty-four kinds of intercellular gap junctions are designed to describe the intercellular excitation propagation among cells of different kinds and cells along different direction. By parallely solving thousands groups of action potential equations of cardiac cells and computing the trans-junctional excitation propagation between every cell pairs, the spatiotemporal process of ca...