心率变异信号的复杂性分析

发布时间：2018-06-29 06:17

本文选题：心率变异 + 排列熵　；参考：《南京大学》2012年硕士论文

【摘要】：心率变异信号包含心血管系统调节的重要信息,很多非线性动力学方法和复杂度分析方法都被应用于该信号的分析中。其中,熵测度分析因其算法的简洁性和有效性成为很多研究者关注的热点。本文主要研究心率变异信号熵测度分析的问题。采用排列熵算法对两个国际标准数据库进行分析,探究某些冥想锻炼如气功和瑜伽是否能够影响人的心脏动力学特征,结果显示这两种冥想锻炼能够降低心率变异的复杂度。本文还提出了改进的排列熵算法,充分考虑到相等状态出现的情况,从而更好地描述系统的状态特征。此外,本文提出了一种新的符号熵分析算法。该算法致力于消除心率变异信号中繁杂的细节而仅仅保留信号中重要的变化信息,采用三种简单的符号来描述系统特征。结果证明这种方法有很好的抗噪声能力,也能更好地区分心率变异信号中不同的生理和病理状态。
[Abstract]:Heart rate variability (HRV) signal contains important information about cardiovascular regulation. Many nonlinear dynamics methods and complexity analysis methods have been applied to the analysis of HRV signals. Among them, entropy measure analysis has become a hot topic for its simplicity and effectiveness. This paper focuses on the analysis of signal entropy measurement of heart rate variability (HRV). The permutation entropy algorithm was used to analyze the two international standard databases to find out whether some meditation exercises such as Qigong and Yoga could affect human cardiac dynamics. The results showed that these two kinds of meditation exercises could reduce the complexity of heart rate variability. This paper also proposes an improved permutation entropy algorithm, which takes into account the occurrence of equal states, so as to better describe the state characteristics of the system. In addition, a new symbol entropy analysis algorithm is proposed. The algorithm is designed to eliminate the complicated details of the HRV signal and only retain the important variation information in the signal. Three simple symbols are used to describe the system features. The results show that this method has good anti-noise ability and can better distinguish different physiological and pathological states in HRV signals.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：R318.0

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