  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.
