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arXiv 1111.5926math.NAcs.NAphysics.med-ph
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心脏电生理的降阶建模:应用于参数识别

Reduced-order modeling for cardiac electrophysiology. Application to parameter identification

Project-Teams REO(REO项目组)

Muriel Boulakia, Elisa Schenone, Jean-Frédéric Gerbeau

AI总结:

提出基于本征正交分解(POD)的降阶模型用于心脏电生理的双域方程,通过心电图评估精度,并应用于逆问题中通过进化算法识别离子参数和梗死位置。

摘要:

针对心脏电生理的双域方程,提出了一种基于本征正交分解(POD)的降阶模型。通过心电图在不同配置下(包括心肌梗死和长时间模拟)评估其准确性。我们特别展示了该方法可以有效地近似恢复曲线。然后将降阶模型用于由进化算法求解的逆问题。展示了一些从合成心电图识别离子参数和梗死位置的尝试。

英文摘要:

A reduced-order model based on Proper Orthogonal Decomposition (POD) is proposed for the bidomain equations of cardiac electrophysiology. Its accuracy is assessed through electrocardiograms in various configurations, including myocardium infarctions and long-time simulations. We show in particular that a restitution curve can efficiently be approximated by this approach. The reduced-order model is then used in an inverse problem solved by an evolutionary algorithm. Some attempts are presented to identify ionic parameters and infarction locations from synthetic ECGs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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