Data Fusion for Inverse Electrocardiography: Synthesis of Signals from Multiple Sensor Types and Locations

0001315 Throne了解心脏表面的电位模式如何随时间演变，在诊断和治疗各种心血管疾病方面是一个非常有价值的工具。 因此，研究人员已经提出了许多微创方法，用于从在更容易接近的位置处进行的电活动的测量来推断心脏电活动。 在这些类型的逆问题中，目标是基于输出的测量（在侵入性较小的位置处测量的电活动）来估计输入（表面电势）。这些特定的逆问题是极其病态的，并且因此测量中的小误差导致估计的心脏电位中的巨大（数百倍大）误差。 该项目的总体目标是采用数据融合-来自多个传感器，传感器类型和传感器位置的信号的系统组合-以改善心脏内外表面的潜在估计。微创、稳定的逆向技术的发展将对心脏疾病的治疗产生重大影响。 此外，为这些逆问题开发的数据融合技术将影响不同领域的研究，如识别大脑内的电活动源，结构损伤识别，电子冷却，材料表征和在线机床磨损监测。

0001315ThroneKnowledge of how electrical potential patterns on the heart surfaces evolve over time can be an extremely valuable tool in the diagnosis and treatment of a wide variety of cardiovascular diseases. Hence, researchers have proposed numerous minimally invasive approaches for inferring cardiac electrical activity from measurements of electrical activity made at more accessible locations. In these types of inverse problems the goal is to estimate an input (the surface electrical potentials) based on a measurement of the output (the electrical activity measured at a less invasive location). These particular inverse problems are extremely ill-conditioned, and consequently small errors in the measurements cause enormous (hundreds of times larger) errors in the estimated cardiac potentials. The overall goal in this project is to employ data fusion-- systematic combination of signals from multiple sensors, types of sensors, and sensor locations-- to improve the potential estimates on the interior and exterior surfaces of the heart. The development of minimally invasive, stable inverse techniques will substantially affect the treatment of cardiac disease. In addition, the data fusion techniques developed for these inverse problems will impact research in such diverse fields as identifying sources of electrical activity within the brain, structural damage identification, electronics cooling, materials characterization, and online machine tool wear monitoring.