Network analysis of heart beat intervals using horizontal visibility graphs

Heart beat interval time series contain information predictive of heart disease, but most current predictors do not provide sufficient reliability for clinical use. Using several predictors improves predictive power, but the limit is not yet known, suggesting that not all the information in interbeat interval series has been captured by previous work. We convert heart beat time series into scale-free networks using horizontal visibility graphs (HVGs), which are well-suited to distinguishing deterministic dynamical systems from stochastic systems, allowing them to model new aspects of autonomic heart rate modulation. Based on the HVG, we introduce and evaluate a general class of predictors, which can be used to augment existing features used in heart rate variability (HRV) analysis, and which exhibit high predictive power for several types of heart disease. We show the statistical significance of these network predictors, and their competitive performance to popular statistical, geometric and non-linear features, on ICU and Holter ECGs, including several heart disease etiologies.