Analysis Of Heart Beat Interval Series Based On Visibility Networks

With the high incidence of heart diseases, modern people pay more and more attention on the health of heart. ECG and DCG are widely used for monitoring and checking the healthy condition of the patients. The main purpose of studying ECG is to provide means of diagnosis of heart disease.This thesis focuses on discovering the statistical quantities underlying the complexity in the human heartbeat dynamics. Firstly, the basic concepts of ECG, R-R interval, CHF and meditation training are briefly introduced. Secondly, the principle of the visibility algorithm and the properties of visibility graph, including the degree distribution, and the assortative mixing are described in details. Then we use visibility algorithm to analyze the R-R interval series of the healthy subjects and the CHF patients, as well as the R-R interval series of the subjects before and during meditation training are analyzed. The results show that visibility algorithm can be used to analyze R-R interval series, and the visibility graph constitutes a reliable and intuitive tool to evaluate the dynamical changes of heartbeat under different physiological and pathological conditions.