Research On Complex Network Topology Of ECG Signal Time Series

The past few years have witnessed great advances in the field of complex networks, which provides profound insights in engineering, social and biological fields. Characterizing the complex dynamical features from experimental time series is a fundamental problem of long term interest in a wide variety of fields. Different measures have been proposed to analyze time series, for example, Lyapunov exponent, entropies, and fractal dimensions.Recently, a bridge between time series analysis and complex networks has emerged, which allows for studying the dynamics of time series with the tools of networks theroy. Complex network measures substantially enrich the knowledge gathered from linear and nonlinear approaches.The thesis is organized as follows.1. We review recent approaches for transforming a time series into a network. In this chapter, we give a brief view and classification of existing transforming methods, including a comprehensive discussion of their potentials and limitations. Finally, we report the present findings of complex network theory applied to cardiovascular time series analysis.2. We study the motif ranks of complex networks induced from different categories of time series with periodic bifurcations and chaos, which are generated with two algorithm:the visibility graph algorithm and the adaptive nearest neighbor network algorithm. The advantages of both algorithms are analyzed.3. We construct networks of human ventricular time series with the visibility graph approach to study the network subgraph phenomenon and motif ranks. Our results show that different dominate motifs exist as an effective indicator to distinguish ventricular fibrillations from normal sinus rhythms of a subject.4. We construct networks induced from heartbeat time series of patient with congestive heart failure and the healthy subjects by the visibility graph, and explore the topology statistics of the associated networks. We conclude that the assortativeness of the networks fails working as an effective indicator to identify a congestive heart failure patient.