Collaborative Research: Intramural Forecasting of Cardiac Electrical Dynamics

合作研究：心脏电动力学的壁内预测

• 批准号: 1234332
• 负责人: Flavio Fenton
• 金额: $ 17万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1234332&HistoricalAwards=false
• 关键词: Collaborative; Research; Intramural; Forecasting; Cardiac

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.

这项研究将促进预防和治疗心律失常的具体方法的设计，也是展示状态估计技术在研究心脏电动力学和其他很少或没有深度信息的 3D 动力系统方面的有效性的重要第一步。 心律失常是由于预先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发的异质性对心脏中正常电波传播的破坏造成的，这两种情况都可能导致电波破裂。现有的在三维实验心脏制备中观察电波的方法会产生稀疏的数据集，要么是详细的表面观察，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。预测方法（以前未用于心脏电模型）将被修改以生成心脏电波三维传播的改进估计。具体来说，在数值天气预报领域开发的数据同化技术将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，以在启动过程中得出更准确、更真实的心脏电波三维量化和心律失常的进展。使用这种方法，将创建更准确的三维时间序列，可用于确定心律失常诱导和维持所涉及的机制。心血管疾病，包括心律失常（如颤动），是工业化国家最常见的死亡原因，对健康和经济产生严重影响。治疗心律失常的进展受到阻碍，部分原因是缺乏对心律失常动力学的了解，这是由于难以在心脏组织厚度内获得观察结果。成功的天气预报技术将用于利用心脏内表面和外表面获得的数据定量推断心脏组织的三维电动力学。这项研究有可能阐明心律失常的机制，并可能带来新的治疗方法接近。此外，作为该项目的一部分所做的工作可能有助于难以获得三维观测的各个领域的预测技术的进步，包括了解脑癌的扩散和深海的动态。