Scale exponents of blood pressure and heart rate during autonomic blockade as assessed by detrended fluctuation analysis

Abstract

Non-technical summary It is still unknown how the autonomic nervous system influences the fractal dynamics of cardiovascular signals. We show that in supine volunteers vagal and sympathetic outflows contribute differently to the fractal structures of heart rate and blood pressure. The vagal outflow contributes with a 'white-noise' component to the heart rate dynamics, indirectly influencing also the fractal dynamics of blood pressure. The sympathetic outflow contributes with a Brownian motion component to the heart rate dynamics, increasing long-term fractal coefficients, without affecting long-term coefficients of blood pressure. Results are explained by the different distribution and dynamics of acetylcholine receptors and of α-and β-adrenergic receptors. Our findings may allow better delineating alterations of cardiovascular fractal dynamics in physiological and pathophysiological settings.Abstract How the autonomic nervous system influences the fractal dynamics of heart rate (HR) and blood pressure (BP) remains unclear. The purpose of our study was to separately assess cardiac vagal and sympathetic (cardiac vs. vascular) influences on fractal properties of HR and BP as described by scale exponents of detrended fluctuation analysis (DFA). R-R intervals, systolic and diastolic BP were measured in nine supine volunteers before and after administration of autonomic blocking agents (atropine, propranolol, atropine + propranolol, clonidine). Spectra of DFA scale exponents, α(t), were calculated for scales between 5 and 100 s. HR and BP scale structures differed at baseline, being α(t) of HR <1, with a minimum between 10 and 20 s followed by a higher plateau between 40 to 80 s, while α(t) of BP decreased with t from values >1. Comparison of atropine and propranolol with baseline and combined cardiac parasympathetic and sympathetic blockade (atropine + propranolol) indicated opposite influences of vagal and cardiac sympathetic outflows on HR exponents. The vagal outflow adds white-noise components, amplifying differences with BP exponents; the cardiac sympathetic outflow adds Brownian motion components at short scales and contributes to the plateau between 40 and 80 s. Overall sympathetic inhibition by clonidine decreased short-and long-term exponents of HR, and short-term exponents of BP, so that their α(t) spectra had different means but similar profiles. Therefore, cardiac vagal, cardiac sympathetic and vascular sympathetic outflows contribute differently to HR and BP fractal structures. Results are explained by different distribution and dynamics of acetylcholine receptors and of α-and β-adrenergic receptors between heart and vasculature. Abbreviations BP, blood pressure; DBP, diastolic blood pressure; DFA, detrended fluctuation analysis; HF, high frequency; HR, heart rate; LF, low frequency; MAP, mean arterial pressure; SBP, systolic blood pressure; VLF, very low frequency.

Multifractal‐Multiscale Analysis of Cardiovascular Signals: A DFA‐Based Characterization of Blood Pressure and Heart‐Rate Complexity by Gender

“…A similar behavior has been previously observed in other studies that applied DFA with traditional monofractal approaches (i.e., with = 2 only) to analyze the heart-rate variability in sitting volunteers at rest [16,28]. A possible explanation for this trend has been already proposed, based on the hypothesis that the heart-rate dynamics depend on the superposition of two fractal processes simultaneously modulating the heart rate [7,29]. One process, with relatively homogeneous fractal characteristics at all scales, resembling fractional Gaussian noise, depends on the cardiac vagal outflow.…”

“…In this way, however, local deviations from the linear trend occurring at specific scales n cannot be detected. The assessment of local deviations may reflect changes in the sympathetic and vagal cardiac control not otherwise visible [7,16], revealing subtle alterations in the overall autonomic regulation of the cardiovascular system [21] and characterizing pathological conditions [6,22]. To evaluate a local slope, that is, as function of n, methods with higher scale resolution are required.…”

“…Therefore, to associate each scale coefficient with its temporal scale, we mapped the beat domain into the time domain [7]. Given the ( , ) coefficients evaluated on the beat domain for the { } set of scales, the coefficients evaluated on the corresponding set of temporal scales, { }, are ( , ) = ( , ) for = × IBI (6) with IBI being the mean IBI, in seconds.…”

“…Successive studies recognized that such an approach oversimplifies a more complex phenomenon, 2 Complexity because the fractal characteristics of heart rate appear to depend on the scale of the observation [5]. This led some authors to propose multiscale approaches that quantify the cardiovascular complexity by a spectrum of self-similarity coefficients evaluated at different temporal scales [6][7][8].…”

Hemodynamic and Autonomic Response to Different Salt Intakes in Normotensive Individuals

“…Frequency‐domain indices of BP variability were the LF power of diastolic BP, reported to reflect sympathetically mediated vasomotor oscillations generally assumed to be induced by baroreflex resonance10, 11; the very‐low frequency power of diastolic BP that quantifies long‐term fluctuations mainly of vasomotor origin; and the sensitivity of baroreflex control of HR estimated by the transfer function method over the LF (BRS LF) and HF (BRS HF) bands. Additionally, we calculated the number of PI increases per minute larger than 50 ms (NN50+ , time‐domain index of cardiac parasympathetic modulation under the hypothesis that bursts of vagal outflow on the sinus node produce PI lengthening of more than 50 ms)12; and the short‐term scale coefficient α 1 through detrended fluctuation analysis of PI, which is a complexity‐domain index of cardiac sympatho/vagal balance 13, 14…”