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 () or a sedentary protocol (). 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 from HRV series. Multiscale SDiff is a recent approach that accounts for entropy differences between a given time series and its shuffled dynamics. From SDiff, three attributes (q-attributes) were derived, namely SDiff, and . MSE, MDE and multiscale q-attributes presented similar profiles, except for SDiff. 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.
Highlights
The study of system complexity is very challenging and has attracted much attention in the past few years [1,2,3]
No difference was found between the groups in the mean values of multiscale entropy (MSE) and multiscale dispersion entropy (MDE) grouped by short (1 to 5) and long (6 to 20) time scales (Figure 1C,D), for higher scales, there was a tendency of increasing differences among groups (Figure 1A,B)
Previous studies have reported on MSE as a powerful tool to assess the complexity of Heart rate variability (HRV) [37,45,46,47,48]
Summary
The study of system complexity is very challenging and has attracted much attention in the past few years [1,2,3]. Physiological complexity reflects the interoperability and correct functioning of regulatory processes as a whole, so the higher the complexity, the higher the system ability to adapt to different situations in daily life [4]. Heart rate variability (HRV) series, derived from the electrocardiogram (ECG) or arterial pressure signals, is one of the most important sources of information about system physiological status. Heart rate is actively controlled by the autonomic nervous system and can respond to many situations when the organism is challenged. One of the most substantial challenges in the quantification of complexity from HRV time series is the difficulty in finding out a single measurement capable of doing this task consistently HRV are powerful risk predictors of morbidity and death, for cardiac and non-cardiac diseases [5,6,7].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
