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

逆心电图数据融合：来自多种传感器类型和位置的信号合成

• 批准号: 0001315
• 负责人: Robert Throne
• 金额: $ 20.7万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2002-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0001315&HistoricalAwards=false
• 关键词: Data; Fusion; Inverse; Electrocardiography; Synthesis

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.

0001315Throne了解心脏表面电势模式如何随时间变化的知识可以成为诊断和治疗各种心血管疾病的极其有价值的工具。 因此，研究人员提出了多种微创方法，通过在更容易到达的位置测量电活动来推断心脏电活动。 在这些类型的反问题中，目标是根据输出的测量（在侵入性较小的位置测量的电活动）来估计输入（表面电势）。这些特定的逆问题条件极其恶劣，因此测量中的小误差会导致估计心电势的巨大（大数百倍）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）来改进对心脏内表面和外表面的潜在估计。微创、稳定的逆向技术的发展将极大地影响心脏病的治疗。 此外，针对这些反问题开发的数据融合技术将影响多个领域的研究，例如识别大脑内电活动的来源、结构损伤识别、电子冷却、材料表征和在线机床磨损监测。