The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure

Abstract

Cardiovascular control acts over multiple time scales, which introduces a significant amount of complexity to heart rate and blood pressure time series. Multiscale entropy (MSE) analysis has been developed to quantify the complexity of a time series over multiple time scales. In previous studies, MSE analyses identified impaired cardiovascular control and increased cardiovascular risk in various pathological conditions. Despite the increasing acceptance of the MSE technique in clinical research, information underpinning the involvement of the autonomic nervous system in the MSE of heart rate and blood pressure is lacking. The objective of this study is to investigate the effect of orthostatic challenge on the MSE of heart rate and blood pressure variability (HRV, BPV) and the correlation between MSE (complexity measures) and traditional linear (time and frequency domain) measures. MSE analysis of HRV and BPV was performed in 28 healthy young subjects on 1000 consecutive heart beats in the supine and standing positions. Sample entropy values were assessed on scales of 1-10. We found that MSE of heart rate and blood pressure signals is sensitive to changes in autonomic balance caused by postural change from the supine to the standing position. The effect of orthostatic challenge on heart rate and blood pressure complexity depended on the time scale under investigation. Entropy values did not correlate with the mean values of heart rate and blood pressure and showed only weak correlations with linear HRV and BPV measures. In conclusion, the MSE analysis of heart rate and blood pressure provides a sensitive tool to detect changes in autonomic balance as induced by postural change.

Effect of the Postural Challenge on the Dependence of the Cardiovascular Control Complexity on Age

“…We confirm that in the youngest group (i.e., 21-30) ΔNCI was positive in the case of ΔNCIHP and was about 0 in the case of ΔNCISAP [43,55]. One of the original findings of this study is that ΔNCIHP decreased with age in the overall population, while ΔNCISAP was not affected by age.…”

“…We suggest that the decrease of ΔNCIHP with age is a sign of the difficulty that older individuals have in dealing with sympathetic stressors, such as the orthostatic challenge [37]: indeed, older subjects seem to lose their ability to decrease HP complexity by synchronizing the activity of several control mechanisms [35], likely via a sympathetic activation [56]. This loss of capability was not evident at the level of complexity of the vascular control, mainly because ΔNCISAP was already low in young subjects [43,55]. These findings are in agreement with those reported in [18,57,58] and can be explained by the reduced effect of the change of posture on the cardiovascular variables with age.…”

A Refined Multiscale Self-Entropy Approach for the Assessment of Cardiac Control Complexity: Application to Long QT Syndrome Type 1 Patients

“…It is well-known that the complexity of HP, as estimated by the RMSCE of HP, is under vagal control at short time scale [12,13,15,42,43] and under sympathetic control at long time scale [12,13,15]. This study confirms these observations.…”

“…In the range of the time scales considered in this study a large variety of reflexes including baroreflex, cardiopulmonary circuits and direct central vagal and sympathetic influences actively regulate HP [25]. These reflexes and autonomic inputs are responsible for the rhythmical fluctuations of HP, usually quantified via spectral analysis [24], and for the large degree of predictability of the HP series, here quantified by RMSsE, especially under sympathetic activation [8,14,15,26]. Conversely, QT regulation is only partially mediated by HP via the QT-HP relation [27,28] and largely independent of sinus node regulation via neural inputs directly acting over the ventricles [29][30][31][32][33][34][35][36][37].…”

“…While the CE is more widely utilized as a measure of complexity of a series [1][2][3], sE is traditionally exploited to assess regularity and predictability of a process [8] or information stored in it [7,9]. CE depends on time scales and this dependence, usually assessed via multiscale conditional entropy (MSCE) approach [11] or via its refinement referred to as refined MSCE (RMSCE) [12], provides relevant information about cardiovascular regulation because it allows the focalization of cardiac control mechanisms acting over an assigned time scale [11][12][13][14][15][16][17]. While the importance of monitoring the CE as a function of the time scale via MSCE or RMSCE is indubitable, it is unclear whether sE is worth to be monitored as a function of the time scale.…”