Collaborative Research: Intramural Forecasting of Cardiac Electrical Dynamics

这项研究将促进预防和治疗心律失常的具体方法的设计，也是证明状态估计技术在研究心脏电动力学和其他三维动力系统方面的有效性的重要的第一步，这些系统的深度信息很少或没有。心律失常是由于先前存在的异质区域或由于快速起搏以及系统的非线性动力学而产生的动态诱导的异质区域破坏了正常的电波在心脏中的传播，这两种情况都可能导致波破裂。现有的在三维实验心脏准备中观察电波的方法产生稀疏的数据集，要么是详细的表面观测数据，要么是来自内部少量点的数据。另一方面，数值模型是研究三维波传播的重要方法，但只是真实动力学的近似。以前未用于心脏电模型的预测方法将被用于对心脏电波的三维传播进行改进的估计。具体来说，在数值天气预报领域发展起来的数据同化技术将被应用于将心脏组织表面电压的时空观测与数值模型结果相结合，从而在心律失常的发生和发展过程中获得更准确和真实的心脏电波三维量化。使用这种方法，将创建一个更精确的三维时间序列，可用于确定心律失常诱导和维持的机制。心血管疾病，包括心房纤颤等心律失常，是工业化国家最常见的死亡原因，对健康和经济造成严重影响。治疗心律失常的进展受到阻碍，部分原因是缺乏对其动力学的理解，这源于在心脏组织厚度内获得观察的困难。从天气预报成功的技术将被应用于推断三维电动力学的心脏组织定量利用数据获得的内部和外部表面的心脏组织。这项研究有可能阐明心律失常的机制，并可能导致新的治疗方法。此外，作为该项目的一部分所做的工作可能有助于提高难以获得三维观测的各种领域的预测技术，包括了解脑癌的扩散和深海动力学。

This research will promote the design of specific methods for preventing and treating arrhythmias and also is an important first step in demonstrating the effectiveness of state estimation techniques for studying cardiac electrical dynamics and other 3D dynamical systems where little or no depth information is available. Cardiac arrhythmias result from the disruption of normal electrical wave propagation in the heart by pre-existing heterogeneous regions or by dynamically induced heterogeneities that develop as a consequence of rapid pacing along with the nonlinear dynamics of the system, both of which can lead to wave breakup. Existing methods for observing electrical waves in three-dimensional experimental heart preparations result in sparse data sets, with either detailed surface observations or data from a small number of points in the interior. Numerical models, on the other hand, are an important method for studying three-dimensional wave propagation, but are only an approximation of the true dynamics. Forecasting methods - previously unused for cardiac electrical models - will be adapted to generate improved estimates of the three-dimensional propagation of cardiac electrical waves. Specifically, data assimilation techniques - developed in the field of numerical weather forecasting - will be applied to combine spatiotemporal observations of voltage on cardiac tissue surfaces with numerical model results to derive a more accurate and realistic three-dimensional quantification of cardiac electrical waves during the initiation and progression of arrhythmias. Using this approach, a more accurate three-dimensional time series that could be used to determine the mechanisms involved in arrhythmia induction and maintenance will be created. Cardiovascular diseases, including cardiac arrhythmias like fibrillation, are the most common cause of death in the industrialized world and have serious health and economic impacts. Advances in treating arrhythmias are hindered in part because of the lack of understanding of their dynamics, which stems from the difficulty of obtaining observations within the thickness of heart tissue. Successful techniques from weather forecasting will be applied to infer the three-dimensional electrical dynamics of cardiac tissue quantitatively using data obtained at the interior and exterior surfaces of the heart.This research has the potential to elucidate mechanisms underlying cardiac arrhythmias and may lead to new treatment approaches. In addition, the work done as part of this project may contribute to advances in forecasting techniques in a variety of fields where three-dimensional observations are difficult to obtain, including understanding the spread of brain cancer and the dynamics of the deep ocean.