发表机构
The Pennsylvania State University; James A. Haley Veterans’ Hospital（宾夕法尼亚州立大学; 詹姆斯·A·海利退伍军人医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出利用多重分形分析和多尺度递归量化等非线性动力学工具，对复杂生理系统（如心血管）进行表征与监测，以应对大数据时代医疗分析需求。
AI 中文摘要
非线性动力学产生于系统中众多实体相互合作、竞争或干扰之时。例如，心血管系统涉及高度的复杂性。多导联心电图信号通过细胞协调一致的去极化和复极化过程产生，并表现出显著的非线性动力学特征。非线性动力学系统难以通过传统的还原论方法来理解，该方法试图通过将已单独分析的所有组成部分组合起来来理解系统的行为。为了应对系统复杂性，现代医疗系统正在投资于先进的生理传感和患者监测技术，从而产生了大数据。要充分发挥大数据在医疗智能方面的潜力，需要全新的方法论来利用和开发复杂性。然而，现有的非线性动力学技术要么不关注医疗分析目标，要么无法有效分析大数据以提取有用信息来改善医疗服务。迫切需要开发能够充分利用生理系统中潜在非线性动力学的分析方法，以推进具有个性化、响应性和卓越质量等特殊特征的医疗服务。本章介绍了一些理论发展和工具，以推进非线性动力学原理在医疗保健中的应用。具体而言，我们专注于基于传感器的非线性动力学表征和建模（即多重分形分析和多尺度递归量化）。然后，考察了这些方法在表征和利用心率变异性及时空心电图信号方面的当前发展和应用。
英文摘要
Nonlinear dynamics arise whenever multifarious entities of a system cooperate, compete, or interfere. For example, cardiovascular system involves a great level of complexity. Multi-lead ECG signals are generated through orchestrated depolarization and repolarization of cells and manifest significant nonlinear dynamics. Nonlinear dynamical systems defy understanding based on the traditional reductionist's approach, in which one attempts to understand a system's behavior by combining all constituent parts that have been analyzed separately. In order to cope with system complexity, modern healthcare systems are investing in advanced physiological sensing and patient monitoring, thereby giving rise to big data. Realizing the full potential of big data for healthcare intelligence requires fundamentally new methodologies to harness and exploit complexity. However, available nonlinear dynamics techniques are either not concerned with healthcare analytical objectives or fail to effectively analyze big data to extract useful information for improving healthcare services. There is an urgent need to develop analytical methodologies that fully exploit the underlying nonlinear dynamics in physiological systems for advancing healthcare services with exceptional features such as personalization, responsiveness, and superior quality. This chapter presents some theoretical developments and tools to advance the applications of nonlinear dynamics principles in health care. Specifically, we focus on sensor-based characterization and modeling of nonlinear dynamics (i.e., multifractal analysis and multiscale recurrence quantification). Then, current developments and applications of these methodologies are examined for characterizing and exploiting heart rate variability and space-time ECG signals.
Journal refIn book: Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, Eds: H. Yang and E. K. Lee, Wiley, March 7, 2016, pp. 59-93
