Evaluating transient phenomena by wavelet analysis: early recovery to exercise.

ABSTRACT

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.