标题： 马尔可夫-二进制可视图：分析复杂系统的新方法 显示英文标题

标题： Markov-Binary Visibility Graph: a new method for analyzing Complex Systems

摘要： 在本工作中，我们引入了一种新的简单的从时间序列到复杂网络的转换方法，基于马尔可夫二进制可视图（MBVG）。 由于这种转换结构简单，与其他转换相比可以得到更精确的结果。 此外，对所构建的图的几个拓扑方面，如度分布、聚类系数和平均可视长度进行了深入研究。 数值模拟证实了马尔可夫二进制可视图在时间序列分析中的可靠性。 该算法能够区分无关联和有关联的系统。 最后，我们展示了该算法在分析人类心跳动力学中的应用。 结果表明，正常人的心跳（RR间隔）时间序列、充血性心力衰竭（CHF）患者和心房颤动（AF）患者的的心跳时间序列分别属于无关联、混沌和有关联的随机系统。 显示英文摘要

摘要： In this work, we introduce a new and simple transformation from time series to complex networks based on markov-binary visibility graph(MBVG). Due to the simple structure of this transformation in comparison with other transformations be obtained more precise results. Moreover, several topological aspects of the constructed graph, such as degree distribution, clustering coefficient, and mean visibility length have been thoroughly investigated. Numerical simulations confirm the reliability of markov-binary visibility graph for time series analysis. This algorithm have the capability of distinguishing between uncorrelated and correlated systems. Finaly, we illustrate this algorithm analyzing the human heartbeat dynamics. The results indicate that the human heartbeat (RR-interval) time series of normally, Congestive Heart Failure (CHF) and Atrial Fibrillation (AF) subjects are uncorrelated, chaotic and correlated stochastic systems, respectively.