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首页> 美国卫生研究院文献>Journal of the Royal Society Interface >Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model
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Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model

机译：细胞自动机模型研究房颤随机发作和终止的机制

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Mathematical models of cardiac electrical excitation are increasingly complex, with multiscale models seeking to represent and bridge physiological behaviours across temporal and spatial scales. The increasing complexity of these models makes it computationally expensive to both evaluate long term (more than 60 s) behaviour and determine sensitivity of model outputs to inputs. This is particularly relevant in models of atrial fibrillation (AF), where individual episodes last from seconds to days, and interepisode waiting times can be minutes to months. Potential mechanisms of transition between sinus rhythm and AF have been identified but are not well understood, and it is difficult to simulate AF for long periods of time using state-of-the-art models. In this study, we implemented a Moe-type cellular automaton on a novel, topologically equivalent surface geometry of the left atrium. We used the model to simulate stochastic initiation and spontaneous termination of AF, arising from bursts of spontaneous activation near pulmonary veins. The simplified representation of atrial electrical activity reduced computational cost, and so permitted us to investigate AF mechanisms in a probabilistic setting. We computed large numbers (approx. 105) of sample paths of the model, to infer stochastic initiation and termination rates of AF episodes using different model parameters. By generating statistical distributions of model outputs, we demonstrated how to propagate uncertainties of inputs within our microscopic level model up to a macroscopic level. Lastly, we investigated spontaneous termination in the model and found a complex dependence on its past AF trajectory, the mechanism of which merits future investigation.
机译：心脏电激发的数学模型越来越复杂，多尺度模型试图在时间和空间尺度上代表和桥接生理行为。这些模型的复杂性不断提高，因此评估长期（超过60 s）行为并确定模型输出对输入的敏感性在计算上非常昂贵。这在房颤（AF）模型中尤为重要，在该模型中，个别发作持续数秒至数天，而片间等待时间可能是数分钟至数月。窦性心律和房颤之间转换的潜在机制已被确定，但尚未得到很好的理解，并且使用最新模型很难长时间模拟房颤。在这项研究中，我们在左心房的一种新颖的，拓扑上等效的表面几何形状上实现了Moe型细胞自动机。我们使用该模型来模拟由肺静脉附近的自发激活爆发引起的房颤的随机启动和自发终止。心房电活动的简化表示降低了计算成本，因此使我们能够在概率环境下研究房颤机制。我们计算了模型的大量样本路径（约10 5 ），以推断使用不同模型参数的AF发作的随机起止率。通过生成模型输出的统计分布，我们演示了如何在微观层次模型内将输入的不确定性传播到宏观层次。最后，我们研究了模型中的自发终止，发现了对其过去AF轨迹的复杂依赖，其机理值得进一步研究。

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