Oral Glucose Load and Human Cutaneous Microcirculation: An Insight into Flowmotion Assessed by Wavelet Transform

Evaluating transient phenomena by wavelet analysis: early recovery to exercise

Wavelet analysis (WA) provides superior time-frequency decomposition of complex signals than conventional spectral analysis tools. To illustrate its usefulness in assessing transient phenomena, we applied a custom-developed WA algorithm to laser-Doppler (LD) signals of the cutaneous microcirculation measured at glabrous (finger pulp) and nonglabrous (forearm) sites during early recovery after dynamic exercise. This phase, importantly contributing to the establishment of thermal homeostasis after exercise cessation, has not been adequately explored because of its complex, transient form. Using WA, we decomposed the LD signals measured during the baseline and early recovery into power spectra of characteristic frequency intervals corresponding to endothelial nitric oxide (NO)-dependent, neurogenic, myogenic, respiratory, and cardiac physiological influence. Assessment of relative power (RP), defined as the ratio between the median power in the frequency interval and the median power of the total spectrum, revealed that endothelial NO-dependent (5.87 early recovery; 1.53 baseline; P = 0.005; Wilcoxon signed-rank test) and respiratory (0.71 early recovery; 0.40 baseline; P = 0.001) components were significantly increased, and myogenic component (1.35 early recovery; 1.83 baseline; P = 0.02) significantly decreased during early recovery in the finger pulp. In the forearm, only the RP of the endothelial NO-dependent (1.90 early recovery; 0.94 baseline; P = 0.009) component was significantly increased. WA presents an irreplaceable tool for the assessment of transient phenomena. The relative contribution of the physiological mechanisms controlling the microcirculatory response in the early recovery phase appears to differ in glabrous and nonglabrous skin when compared with baseline; moreover, the endothelial NO-dependent influence seems to play an important role.NEW & NOTEWORTHY We address the applicability of wavelet analysis (WA) in evaluating transient phenomena on a model of early recovery to exercise, which is the only exercise-associated phase characterized by a distinct transient shape and as such cannot be assessed using conventional tools. Our WA-based algorithm provided a reliable spectral decomposition of laser-Doppler (LD) signals in early recovery, enabling us to speculate roughly on the mechanisms involved in the regulation of skin microcirculation in this phase.

Wavelet analysis of laser Doppler microcirculatory signals: Current applications and limitations

Laser Doppler flowmetry (LDF) has long been considered a gold standard for non-invasive assessment of skin microvascular function. Due to the laser Doppler (LD) microcirculatory signal's complex biological and physiological context, using spectral analysis is advisable to extract as many of the signal's properties as feasible. Spectral analysis can be performed using either a classical Fourier transform (FT) technique, which has the disadvantage of not being able to localize a signal in time, or wavelet analysis (WA), which provides both the time and frequency localization of the inspected signal. So far, WA of LD microcirculatory signals has revealed five characteristic frequency intervals, ranging from 0.005 to 2 Hz, each of which being related to a specific physiological influence modulating skin microcirculatory response, providing for a more thorough analysis of the signals measured in healthy and diseased individuals. Even though WA is a valuable tool for analyzing and evaluating LDF-measured microcirculatory signals, limitations remain, resulting in a lack of analytical standardization. As a more accurate assessment of human skin microcirculation may better enhance the prognosis of diseases marked by microvascular dysfunction, searching for improvements to the WA method is crucial from the clinical point of view. Accordingly, we have summarized and discussed WA application and its limitations when evaluating LD microcirculatory signals, and presented insight into possible future improvements. We adopted a novel strategy when presenting the findings of recent studies using WA by focusing on frequency intervals to contrast the findings of the various studies undertaken thus far and highlight their disparities.

Vascular ageing and peripheral pulse: an improved model for assessing their relationship

Objective.Vascular ageing is associated with several alterations, including arterial stiffness and endothelial dysfunction. Such alterations represent an independent factor in the development of cardiovascular disease (CVD). In our previous works we demonstrated the alterations occurring in the vascular system are themselves reflected in the shape of the peripheral waveform; thus, a model that describes the waveform as a sum of Gaussian curves provides a set of parameters that successfully discriminate betweenunder(≤35 years old) andoversubjects (>35 years old). In the present work, we explored the feasibility of a new decomposition model, based on a sum of exponential pulses, applied to the same problem.Approach.The first processing step extracts each pulsation from the input signal and removes the long-term trend using a cubic spline with nodes between consecutive pulsations. After that, a Least Squares fitting algorithm determines the set of optimal model parameters that best approximates each single pulse. The vector of model parameters gives a compact representation of the pulse waveform that constitutes the basis for the classification step. Each subject is associated to his/her 'representative' pulse waveform, obtained by averaging the vector parameters corresponding to all pulses. Finally, a Bayesan classifier has been designed to discriminate the waveforms of under and over subjects, using the leave-one-subject-out validation method.Main results.Results indicate that the fitting procedure reaches a rate of 96% in under subjects and 95% in over subjects and that the Bayesan classifier is able to correctly classify 91% of the subjects with a specificity of 94% and a sensibility of 84%.Significance.This study shows a sensible vascular age estimation accuracy with a multi-exponential model, which may help to predict CVD.