Huffel, “Nonlinear heart rate variability in a healthy population

Abstract Heart rate variability (HRV) measurements are used as markers of autonomic modulation of heart rate. Numerical noise titration was applied to a large healthy population to examine the influence of age. Increasing age was associated with decreasing nonlinear behaviour and this age dependency was especially prominent during daytime and was also more pronounced in the female population. Introduction Heart rate variability (HRV) measurements are used as markers of autonomic modulation of heart rate [1]. Standard time and frequency domain methods of HRV are well described by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology Methods Data acquisition Twenty-four hour ECG recordings of 276 healthy subjects (135 women and 141 men between 18 and 74 years of age) were obtained in Leuven (Belgium) using Holter monitoring. After R peak detection and visual inspection by the operator for verifying the peak detection, a file containing the consecutive RR intervals, called tachogram, was exported for later processing. The 24-h recordings were split into daytime (8-21h) and nighttime (23-6h). A detailed medical history was obtained from each participant. More details concerning the study population, monitoring and preprocessing are described in [6]. Nonlinear HRV parameters To assess the nonlinear HRV properties, several methods have been proposed in the past and are calculated here: 1/f slope, fractal dimension (FD), detrended fluctuation analysis (DFA), correlation dimension (CD), approximate entropy (ApEn) and Lyapunov exponent (LE). Numerical noise titration is a nonlinear data analysis technique that is a better alternative for LE, which is a measure of the exponential divergence of nearby states. LE fails to specifically distinguish chaos from noise and can not detect chaos reliably unless the data series are inordinately lengthy and virtually free of noise. However, those requirements are difficult, if not impossible, to fulfill for most empirical data. The different steps of the numerical noise titration algorithm are already well described in Modeling. For any heartbeat RR time series y n , n = 1, 2, · · · , N , a closed-loop version of the dynamics is proposed in which the output y n feeds back as a delayed input. The univariat