Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics
Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics
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DOI：
10.1098/rsta.2019.0388
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发表时间：
2020-06-12
期刊：
影响因子：
5
通讯作者：
Cherry, Elizabeth M.
中科院分区：
文献类型：
--
作者：
Hoffman, Matthew J.;Cherry, Elizabeth M.
心脏电行为的建模带来了重要的机制见解，但重要的挑战，包括模型公式和参数值的不确定性，使得难以获得定量准确的结果。另一种方法是将模型与实验观察相结合，以随着时间的推移生成系统状态的数据知情重建。在这里，我们使用集合卡尔曼滤波器扩展了我们早期的数据同化研究，仅使用电压的表面观测来重建具有复杂时空动力学的三维时间序列。我们考虑几个算法和模型参数对使用合成观测重建已知滚动波真值状态的准确性的影响。特别是，我们研究了算法对控制过程不同部分的参数的敏感性及其对多种模型误差条件的鲁棒性。我们发现该算法在许多情况下都可以达到可接受的误差水平，而最弱的性能出现在模型误差情况和具有更复杂动态的更极端参数状态下。对表现最差情况的分析表明，当集合分布减小时，误差最初会减少，随后会增加。我们的结果提出了通过结合附加膨胀或使用参数或多模型集成来增加集成扩展的进一步改进的途径。本文是主题“心脏和心血管建模和模拟中的不确定性量化”的一部分。
Modelling of cardiac electrical behaviour has led to important mechanistic insights, but important challenges, including uncertainty in model formulations and parameter values, make it difficult to obtain quantitatively accurate results. An alternative approach is combining models with observations from experiments to produce a data-informed reconstruction of system states over time. Here, we extend our earlier data-assimilation studies using an ensemble Kalman filter to reconstruct a three-dimensional time series of states with complex spatio-temporal dynamics using only surface observations of voltage. We consider the effects of several algorithmic and model parameters on the accuracy of reconstructions of known scroll-wave truth states using synthetic observations. In particular, we study the algorithm's sensitivity to parameters governing different parts of the process and its robustness to several model-error conditions. We find that the algorithm can achieve an acceptable level of error in many cases, with the weakest performance occurring for model-error cases and more extreme parameter regimes with more complex dynamics. Analysis of the poorest-performing cases indicates an initial decrease in error followed by an increase when the ensemble spread is reduced. Our results suggest avenues for further improvement through increasing ensemble spread by incorporating additive inflation or using a parameter or multi-model ensemble.This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.