SYMBOLIC DYNAMICS ANALYSIS OF COMPLEX PHYSIOLOGIC TIME SERIES

复杂生理时间序列的符号动力学分析

• 批准号: 7366532
• 负责人: CHUNG-KANG PENG
• 金额: $ 1.21万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2007-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7366532
• 关键词: SYMBOLIC; DYNAMICS; ANALYSIS; COMPLEX; PHYSIOLOGIC

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.

该子项目是利用 NIH/NCRR 资助的中心拨款提供的资源的众多研究子项目之一。子项目和研究者 (PI) 可能已从另一个 NIH 来源获得主要资金，因此可以在其他 CRISP 条目中得到体现。列出的机构是中心的机构，不一定是研究者的机构。复杂的生理信号可能带有与其潜在机制相关的独特的动力学特征。我们提出了一种基于自然语言研究中使用的符号序列的排序统计的方法，以研究不同类型的生理动力学的概况。我们将此方法应用于心率动态，这是一个重要的生理控制系统的输出。该方法可以区分生理、病理状态以及衰老产生的模式。此外，我们观察到生理衰老和病理状态的随机性增加，并且还发现了心室对房颤的反应的非随机模式。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产生影响。