SYMBOLIC DYNAMICS ANALYSIS OF COMPLEX PHYSIOLOGIC TIME SERIES

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。复杂的生理信号可能携带与其潜在机制相关的独特动态签名。我们提出了一种方法，基于秩序统计的符号序列的研究中使用的自然语言，调查不同类型的生理动力学的配置文件。我们将此方法应用于心率动力学，一个重要的生理控制系统的输出。该方法可以区分从生理，病理状态，以及从老化产生的模式。此外，我们观察到生理老化和病理状态的随机性增加，也发现了心房颤动心室反应的非随机模式。这项新技术可能对健康和疾病中的心率控制以及床边诊断有影响。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Complex physiologic signals may carry unique dynamical signatures that are related to their underlying mechanisms. We present a method, based on rank order statistics of symbolic sequences used in the study of nature language, to investigate the profile of different type of physiologic dynamics. We applied this method to heart rate dynamics, an output of an important physiologic control system. The method can discriminate patterns generated from physiologic, pathologic states, as well as from aging. Furthermore, we observed increased randomness in physiologic aging and pathologic states and also uncovered nonrandom patterns in ventricular response to atrial fibrillation. This new technique may have implications of heart rate control in health and disease, and bedside diagnosis.