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

TL;DR

This paper introduces the Markov-Binary Visibility Graph, a new method transforming time series into complex networks, which effectively distinguishes between different types of systems and analyzes human heartbeat dynamics.

Contribution

The paper presents a novel, simple transformation method for time series analysis that improves accuracy and distinguishes between uncorrelated, correlated, and chaotic systems.

Findings

Successfully differentiates uncorrelated, correlated, and chaotic systems

Reliable analysis of human heartbeat dynamics

Effective in distinguishing different physiological states

Abstract

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.