Refined Multiscale Fuzzy Entropy to Analyse Post-Exercise Cardiovascular Response in Older Adults With Orthostatic Intolerance

Abstract

Orthostatic intolerance syndrome occurs when the autonomic nervous system is incapacitated and fails to respond to the demands associated with the upright position. Assessing this syndrome among the elderly population is important in order to prevent falls. However, this problem is still challenging. The goal of this work was to determine the relationship between orthostatic intolerance (OI) and the cardiovascular response to exercise from the analysis of heart rate and blood pressure. More specifically, the behavior of these cardiovascular variables was evaluated in terms of refined composite multiscale fuzzy entropy (RCMFE), measured at different scales. The dataset was composed by 65 older subjects, 44.6% (n = 29) were OI symptomatic and 55.4% (n = 36) were not. Insignificant differences were found in age and gender between symptomatic and asymptomatic OI participants. When heart rate was evaluated, higher differences between groups were observed during the recovery period immediately after exercise. With respect to the blood pressure and other hemodynamic parameters, most significant results were obtained in the post-exercise stage. In any case, the symptomatic OI group exhibited higher irregularity in the measured parameters, as higher RCMFE levels in all time scales were obtained. This information could be very helpful for a better understanding of cardiovascular instability, as well as to recognize risk factors for falls and impairment of functional status.

Associations between Neurocardiovascular Signal Entropy and Physical Frailty

“…Moreover, the similarity between BP and HR entropy measures, in relation to frailty status, suggests that these measures may provide complementary information, as has been previously reported [ 54 ], and as such a univariant approach (i.e., the assessment of one of these measures) may be sufficient for clinical use. Input parameters and implementation of ApEn and SampEn calculations were based on recommendations for similar physiological data from previous studies ( m = 2 (also reported in Appendix B m = 3, 4) [ 14 , 35 ]; r = 0.15 (SampEn) [ 37 , 38 ], optimum calculated [0 to 0.6] (ApEn) [ 14 ]; N > 200 [ 24 , 35 ]); however, a consensus with regards the optimal methodologies to use, as well as normative age- and sex-adjusted reference values, would be required for widespread clinical adoption. Further work is necessary to establish the prognostic implications of entropy measures vis-à-vis other clinical markers (e.g., for the prediction of mortality and other adverse health events).…”

“…SampEn [ 17 ] was calculated as however, in this instance, does not count self-matches. For SampEn, an r of 0.15 was selected, in line with previous recommendations for similar physiological data [ 37 , 38 ]. To assess the effects of data stationarity on entropy measures, we calculated ApEn and SampEn for both the original raw and transformed data.…”

Associations between Cardiovascular Signal Entropy and Cognitive Performance over Eight Years

“…In this study, m (embedding dimension; the length of the data segment being compared) was set to 2, as this has been shown to provide good statistical validity for SampEn measurements, especially for biological data [20,21]. An r (similarity criterion) of 0.15 was selected in line with previous recommendations for similar physiological data [22,23]. To assess the effects of data stationarity on entropy measures, SampEn was calculated for both the original and transformed one-minute data.…”

A Novel Fault Detection Scheme Using Improved Inherent Multiscale Fuzzy Entropy With Partly Ensemble Local Characteristic-Scale Decomposition

“…Please refer to the literatures [20], [21] for the details. The relevant research have shown that RCMFE is more reliable than MFE technique and has widely used in varying fields [12], [21], [34]. However, the shorten-data problem still exists unavoidably in the procedure.…”