论文部分内容阅读
多尺度多变量样本熵评价同步多通道数据的多变量复杂度,是非线性动态相互关系的一种反映,但其统计稳定性差,且不适用于非线性非平稳信号.研究利用模糊隶属度函数代替模式相似判断的硬阈值准则,并分析模糊隶属度函数形式的影响;研究利用多变量经验模态分解算法进行多尺度化,并对比其处理效果.仿真试验表明,模糊隶属度函数的引入可以有效提高算法的统计稳定性,所构造的物理模糊隶属度函数的性能最为显著;基于多变量经验模态分解算法的多尺度化过程可更有效地捕获信号的不同尺度成分,从而更敏感地区分具有不同复杂度的信号.对临床试验数据的分析支持以上结论,且结果提示随着年龄增加或心脏疾病的发生,心率变异性和心脏舒张间期变异性的多变量复杂度以不同的方式降低:年龄增加会使低尺度熵值降低,表示近程相关性的丢失;而心脏疾病会同时影响各个尺度的熵值,即同时丢失了近程和长时相关性.该结论可用于指导心血管疾病的无创预警研究.
Multi-scale multivariate sample entropy evaluation of multivariate complexity of simultaneous multi-channel data is a reflection of the nonlinear dynamic relationship, but its statistical stability is poor and not suitable for nonlinear non-stationary signals.Using the fuzzy membership function instead of Mode similarity judgment, and analyze the influence of the form of fuzzy membership function.Multi-variable empirical mode decomposition algorithm is used to study the multi-scale and to compare the processing results.The simulation results show that the introduction of fuzzy membership function can be effective The statistical stability of the algorithm is improved and the performance of the physical fuzzy membership function constructed is the most significant. The multi-scale process based on the multivariate empirical mode decomposition algorithm can capture different scale components of the signal more effectively so as to more sensitively distinguish the The analysis of clinical trial data supports the above conclusion and suggests that the multivariate complexity of heart rate variability and diastolic variability may decrease in different ways with age or with heart disease: Increasing age will reduce the low-scale entropy, indicating the loss of short-range correlation; and heart disease At the same time the influence entropy value of each scale, that while the short- and long-lost relevance. The results can be used for early warning of cardiovascular disease research to guide non-invasive.