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Data Modeling and Aggregation for Medical Monitoring Systems.

机译：医疗监控系统的数据建模和汇总。

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摘要

For decades, we have observed Moore's law in action, shrinking digital electronic devices and endowing them with more and more computing power. Simultaneously, interest increases in the design of protocols, hardware and software components, and applications for networked embedded systems. The use of wireless communication links and the addition of sensing and actuation devices has encouraged their use in many domains in science and industry. A broad scope of applications and devices that provide health and medical services, often termed telehealth, wireless health, or medical monitoring, have benefitted from these developments and are an very active area of research.;Architects of these systems are forced to consider the data to be collected and the end-user experience. Where the embedded system design is concerned, optimization for resource conservation---for example, energy dissipation or network bandwidth---can be performed independently of the characteristics of the data being collected. Building and optimizing models of the signal measurements is paramount when scientific investigation is relevant. Models can be optimized for their accuracy in representing patterns present in the data, and their complexity can be reduced to make them efficient when they are used in production systems. In the quest for simpler models, we hope to help find simpler scientific explanations for the phenomena under study.;This dissertation considers challenges existing in both the signal processing and networking domains. In both cases there are obstacles to be met with methods to efficiently aggregate over a collection of objects of interest; in the networked embedded system design case, we consider efficient aggregation of data measurements made at individual nodes, while in the data modeling case, we consider efficient and accurate methods to aggregate, or group, variables in models. We develop and test protocols to allow arrays of wireless-networked, medical-monitoring nodes to scale gracefully when they communicate, and we also present model selection techniques that facilitate more accurate and precise models, specifically for medical monitoring systems and their associated physiological signals.
机译：数十年来，我们一直在观察摩尔定律的作用，它在缩小数字电子设备并赋予它们越来越大的计算能力的同时。同时，人们对网络嵌入式系统的协议，硬件和软件组件以及应用程序的设计越来越感兴趣。无线通信链路的使用以及传感和致动设备的添加，鼓励了它们在科学和工业的许多领域中的使用。提供健康和医疗服务的广泛应用和设备（通常称为远程医疗，无线健康或医疗监控）已从这些发展中受益，并且是一个非常活跃的研究领域。这些系统的建筑师被迫考虑数据以及最终用户的体验。在嵌入式系统设计方面，可以独立于所收集数据的特性来执行资源节约的优化（例如，能耗或网络带宽）。在进行科学研究时，构建和优化信号测量模型至关重要。可以优化模型的准确性，以表示数据中存在的模式，并且可以降低模型的复杂性，以使其在生产系统中使用时高效。在寻求更简单的模型的过程中，我们希望能够为正在研究的现象找到更简单的科学解释。;本文考虑了信号处理和网络领域中都存在的挑战。在这两种情况下，在一组感兴趣的对象上进行有效聚合的方法都存在障碍。在网络嵌入式系统设计案例中，我们考虑对各个节点进行的数据测量的有效汇总，而在数据建模案例中，我们考虑对模型中的变量进行聚合或分组的有效且准确的方法。我们开发和测试协议，以使无线网络医疗监视节点的阵列在通信时可以适当扩展，并且我们还提供了模型选择技术，可促进更准确，更精确的模型，特别是针对医疗监视系统及其相关的生理信号。

著录项

  • 作者

    Macbeth, Jamie.;

  • 作者单位

    University of California, Los Angeles.;

  • 授予单位 University of California, Los Angeles.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 154 p.
  • 总页数 154
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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