Letellier Christophe, Leyva I, Sendiña-Nadal I
Rouen Normandie University-CORIA, Avenue de l'Université, F-76800 Saint-Etienne du Rouvray, France.
Complex Systems Group & GISC, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain and Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain.
Phys Rev E. 2020 Feb;101(2-1):022204. doi: 10.1103/PhysRevE.101.022204.
We propose a metric to characterize the complex behavior of a dynamical system and to distinguish between organized and disorganized complexity. The approach combines two quantities that separately assess the degree of unpredictability of the dynamics and the lack of describability of the structure in the Poincaré plane constructed from a given time series. As for the former, we use the permutation entropy S_{p}, while for the latter, we introduce an indicator, the structurality Δ, which accounts for the fraction of visited points in the Poincaré plane. The complexity measure thus defined as the sum of those two components is validated by classifying in the (S_{p},Δ) space the complexity of several benchmark dissipative and conservative dynamical systems. As an application, we show how the metric can be used as a powerful biomarker for different cardiac pathologies and to distinguish the dynamical complexity of two electrochemical dissolutions.
我们提出一种度量方法,用于刻画动态系统的复杂行为,并区分有组织的复杂性和无组织的复杂性。该方法结合了两个量,分别评估动力学的不可预测程度以及从给定时间序列构建的庞加莱平面中结构的不可描述性。对于前者,我们使用排列熵(S_{p}),而对于后者,我们引入一个指标——结构度(\Delta),它表示庞加莱平面中被访问点的比例。如此定义为这两个分量之和的复杂性度量,通过在((S_{p},\Delta))空间中对几个基准耗散和保守动态系统的复杂性进行分类得到了验证。作为一个应用,我们展示了该度量如何用作不同心脏疾病的强大生物标志物,以及区分两种电化学溶解的动态复杂性。