Abstract
The aging process may result in attenuated microvascular reactivity in response to environmental stimuli, which can be evaluated by analyzing skin blood flow (SBF) signals. Among various methods for analyzing physiological signals, sample entropy (SE) is commonly used to quantify the degree of regularity of time series. However, we found that for temporally correlated data, SE value depends on the sampling rate. When data are oversampled, SE may give misleading results. To address this problem, we propose to modify the definition of SE by using time-lagged vectors in the calculation of the conditional probability that any two vectors of successive data points are within a tolerance r for m points remain within the tolerance at the next point. The lag could be chosen as the first minimum of the auto mutual information function. We tested the performance of modified SE using simulated signals and SBF data. The results showed that modified SE is able to quantify the degree of regularity of the signals regardless of sampling rate. Using this approach, we observed a more regular behavior of blood flow oscillations (BFO) during local heating-induced maximal vasodilation period compared to the baseline in young and older adults and a more regular behavior of BFO in older adults compared to young adults. These results suggest that modified SE may be useful in the study of SBF dynamics.