基于符号化部分互信息熵的多参数生物电信号的耦合分析

Coupling analysis of multivariate bioelectricity signal based symbolic partial mutual information

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.

Symbolic partial mutual information is proposed in this paper, which is based on partial mutual information. This algorithm can be used to analyse the coupling between multivariate time series. We use this method to treat and analyse the sleeping multivariate bioelectricity signal （MBS） and wake one, it turns out that the coupling of wake MBS is obviously bigger than that of sleeping MBS. Finally hypothesis testing is done to prove that this method works and the average energy dissipation can be used as a parameter to detect nonequilibrium.