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提出了一种新的时间序列耦合信息分析方法——基于部分互信息符号化部分互信息熵.研究表明,多参量的生物电信号各参量间具有耦合关系,使用符号化的部分互信息能够很好地对生物电信号时间序列进行分析,从而获得其耦合程度.应用该算法对生物电信号计算并进行假设检验,结果表明清醒期的生物电信号耦合程度显著高于睡眠期,证明符号化部分互信息可以用来分析时间序列间的耦合信息,而且生物电信号的耦合程度可以作为度量一个物理过程是否处于活跃状态的参数,未来可以应用于临床医学以及生物电传感器等领域.
A new method of time-series coupling information analysis is proposed based on the mutual information entropy of the partial symbolized by mutual information.The research shows that there is a coupling relationship between the parameters of the multi-parameter bioelectric signals and the use of symbolized partial mutual information can The time sequence of bioelectricity signal is well analyzed to obtain the degree of coupling.Application of this algorithm to the bioelectricity signal calculation and hypothesis testing results show that the bioelectricity signal coupling during the awake period is significantly higher than that of the sleeping period, Mutual information can be used to analyze the coupling information between time series, and the coupling degree of bioelectric signals can be used as a parameter to measure whether a physical process is active or not. In the future, it can be applied to fields such as clinical medicine and bioelectric sensors.