Changes in the Complexity of Heart Rate Variability with Exercise Training Measured by Multiscale Entropy-Based Measurements

Abstract

Quantifying complexity from heart rate variability (HRV) series is a challenging task, and multiscale entropy (MSE), along with its variants, has been demonstrated to be one of the most robust approaches to achieve this goal. Although physical training is known to be beneficial, there is little information about the long-term complexity changes induced by the physical conditioning. The present study aimed to quantify the changes in physiological complexity elicited by physical training through multiscale entropy-based complexity measurements. Rats were subject to a protocol of medium intensity training (n = 13) or a sedentary protocol (n = 12). One-hour HRV series were obtained from all conscious rats five days after the experimental protocol. We estimated MSE, multiscale dispersion entropy (MDE) and multiscale SDiff q from HRV series. Multiscale SDiff q is a recent approach that accounts for entropy differences between a given time series and its shuffled dynamics. From SDiff q , three attributes (q-attributes) were derived, namely SDiff q max , q max and q zero. MSE, MDE and multiscale q-attributes presented similar profiles, except for SDiff q max. q max showed significant differences between trained and sedentary groups on Time Scales 6 to 20. Results suggest that physical training increases the system complexity and that multiscale q-attributes provide valuable information about the physiological complexity.

A Heartbeat Away From Consciousness: Heart Rate Variability Entropy Can Discriminate Disorders of Consciousness and Is Correlated With Resting-State fMRI Brain Connectivity of the Central Autonomic Network

“…The causal, directed interactions between brain regions at rest (brain-brain networks) and between resting-state brain activity and the ANS outflow (brain-heart links) have been studied by Duggento et al ( 120 ) showing that the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions are involved in modulating the CAN. Previous studies reported that CI s is probably linked to the vagal control of HRV, while CI l seems to be more related (although not exclusively) to the sympathetic control of HRV ( 41 , 78 , 121 , 122 ).…”

A Pilot Study of the Reliability and Agreement of Heart Rate, Respiratory Rate and Short-Term Heart Rate Variability in Elite Modern Pentathlon Athletes

“…The living organism can be considered a complex system and, as such, it is difficult to disentangle the influences caused by each mechanism, once their functions are highly interdependent [ 90 ]. This is the reason why many nonlinear HRV parameters are considered as “complexity measures”, in the sense that, although they are not clearly associated with specific physiological meanings, they are able to represent the general complexity of the system, attested by their important role as risk factors, prognosis and fitness [ 91 , 92 ].…”

The control mechanisms of heart rate dynamics in a new heart rate nonlinear time series model

“…When myocardial electrical resistance and autonomic nervous balance remain unchanged, the complexity of heart rate dynamics is due primarily to the sufficiency of myocardial metabolism demand relative to oxygen and nutrient supply. This can explain why physical training increases the complexity of HRV 61 and why the resting energy expenditure displays significantly positive correlations with heart rate dynamics 62 .…”