The reconstruction of electrical excitation patterns through the unobserved depth of the tissue is essential to realizing the potential of computational models in cardiac medicine. We have utilized experimental optical-mapping recordings of cardiac electrical excitation on the epicardial and endocardial surfaces of a canine ventricle as observations directing a local ensemble transform Kalman Filter (LETKF) data assimilation scheme. We demonstrate that the inclusion of explicit information about the stimulation protocol can marginally improve the confidence of the ensemble reconstruction and the reliability of the assimilation over time. Likewise, we consider the efficacy of stochastic modeling additions to the assimilation scheme in the context of experimentally derived observation sets. Approximation error is addressed at both the observation and modeling stages, through the uncertainty of observations and the specification of the model used in the assimilation ensemble. We find that perturbative modifications to the observations have marginal to deleterious effects on the accuracy and robustness of the state reconstruction. Further, we find that incorporating additional information from the observations into the model itself (in the case of stimulus and stochastic currents) has a marginal improvement on the reconstruction accuracy over a fully autonomous model, while complicating the model itself and thus introducing potential for new types of model error. That the inclusion of explicit modeling information has negligible to negative effects on the reconstruction implies the need for new avenues for optimization of data assimilation schemes applied to cardiac electrical excitation.

翻译：通过未观测组织深度的电兴奋模式重建对于实现计算模型在心脏医学中的潜力至关重要。我们利用犬心室心外膜和心内膜表面心脏电兴奋的实验光学标测记录作为观测值，引导局部集合变换卡尔曼滤波（LETKF）数据同化方案。研究表明，明确包含刺激协议信息可略微提高集合重建的置信度及同化随时间的可靠性。同时，我们探讨了在实验观测集背景下向同化方案添加随机建模的效用。通过观测不确定性及同化集合所用模型的规范，在观测与建模阶段均处理了近似误差。我们发现，对观测的摄动性修改对状态重建的准确性和鲁棒性产生微弱至有害的影响。此外，将观测中的额外信息纳入模型本身（如刺激电流和随机电流）时，与完全自主模型相比，重建精度仅获得边际提升，但模型复杂度增加，从而引入新型模型误差的潜在风险。明确建模信息的纳入对重建产生可忽略甚至负面影响，这暗示需要开辟新的途径来优化应用于心脏电兴奋的数据同化方案。