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

了解心脏表面的电位模式是如何随时间演变的，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，研究人员提出了许多微创方法，通过在更容易接近的位置测量心脏电活动来推断心脏电活动。在这些类型的反问题中，目标是基于对输出（在侵入性较小的位置测量的电活动）的测量来估计输入（表面电位）。这些特殊的反问题是极端病态的，因此测量中的小误差会导致估计心脏电位的巨大（数百倍大）误差。该项目的总体目标是采用数据融合，系统地组合来自多个传感器、传感器类型和传感器位置的信号，以提高对心脏内外表面的潜在估计。微创、稳定的逆技术的发展将对心脏疾病的治疗产生实质性的影响。此外，为这些逆向问题开发的数据融合技术将影响诸如识别大脑内电活动来源、结构损伤识别、电子冷却、材料表征和在线机床磨损监测等不同领域的研究。

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.