一种统计复杂性测度及在心率变异信号分析中的应用

统计复杂性率量方法是作为结构或关联的一般性指示被提出来的。最近，Ｌｏｐｅｚ－Ｒｕｉｚ等提出一种称为ＣＬＭＣ的统计复杂性测度，满足在秩序和随机两个极端情况下测度为０的边界条件。Ｄａｖｉｄ详细研究了ＣＬＭＣ的特性，发现它既不是一个热力学集中变量也不是一个热力学扩张变量，并提出一种满足热力学扩张特性的补偿形式，但最后证明ＣＬＭＣ只是熵密度的普通解，不能作为结构测度。因此统计复杂性度量不仅应满足有序－随机

A STATISTICAL COMPLEXITY MEASURE AND ITS APPLICATIONSTO THE ANALYSIS OF HEART RATE VARIABILITY

Statistical complexity measures are proposed as general indicators of structure or correlation. Recently, R. Lopez-Ruiz introduced another measure of statistical complexity CLMC that, satisfies the boundary conditions of vanishing in the extreme ordered and disordered limits. David P. Feldman examined some properties of CLMC and found that it is neither an intensive nor extensive thermodynamic variable, and proposed a simple alteration of CLMC that renders its extensive. However the remedy results in a quantity that is a trivial solution of the realizes compensatory form of density and not as a measure of structure. For this reason, a statistical complexity measure should not only satisfy the boundary condition of order-random but also obviously shows described structure. Therefore, we alter the disequilibrium term by the information of time irreversibility (information of nonlinear sequence dynamics) and present a novel statistical complexity measure which is used to quantify the complexity caused by nonlinear dynamics. This statistical complexity measure allows reliable detection of periodic, quais-periodic, linear stochastic and chaotic dynamics. When it is applied to analysis of heart rate data, the estimated statistical complexity appears to be correlated with different cardiac dynamics.