Abstract

BackgroundMany arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level.MethodsWe have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG.ResultsWe demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given.ConclusionsQuantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described.

Highlights

  • Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and evolve spatio-temporally within the heart

  • Factors that influence performance of the parallelized cellular automata model For the physically parallelized whole-heart electrophysiological model built with the extended cellular automata, several factors impair the simulation efficiency on a cluster of shared-memory multiprocessors (SMP)

  • We find that using this strategy, the 3-D heart model cannot reach the best load balance and performance because different nodes deal with different numbers of cardiac cells

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Summary

Objectives

The aim of this paper is to introduce the method and the whole-heart electrophysiological model

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