Linear and Nonlinear Properties of Heart Rate Variability: Influence of Obesity

Physiological systems are best characterized as time-varying processes exhibiting rhythmic and complex behavior. The interaction among system variables, external noise, and state changes modulates the overall variability of physiological signals such as heart rate, arterial pressure, and respiration, which may therefore present both linear and nonlinear patterns. To describe the complex and periodic dynamics of living systems, various analytical tools have been employed, especially in the cardiovascular field.1 Among them, power spectral analysis (PSA)2 and recurrence quantification analysis (RQA)3,4 have been used to describe, respectively, linear and nonlinear dynamics of heart rate variability (HRV). PSA is a validated method that quantifies autonomic nervous modulation of cardiac activity by describing the fluctuations of HR linked to vasomotion and respiration. RQA evaluates complexity and determinism in time series by detecting state changes in drifting or exciting dynamical systems. RQA can be easily applied to cardiovascular signals because it does not require any a priori mathematical assumption, such as stationarity or linearity; parameters introduced by RQA, based on distance, recurrence, and entropy of recurrence plots (RP),5 may be related to different physiological states. Nevertheless, no correlation has been shown between RQA parameters and autonomic nervous activity. It has recently been shown that obesity is a state of reduced sensitivity of the sinoatrial node to both sympathetic and vagal influences.6 Data from obese and lean subjects were therefore analyzed by PSA and RQA, and parameters derived by the two methods were compared for the two groups of subjects.