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.

Keywords: laser-Doppler flowmetry; microcirculation; recovery to dynamic exercise; transient phenomena; wavelet analysis.

Figure 1.

A: three-dimensional (3-D) wavelet spectrum of the laser-Doppler signal of a representative tracing (baseline phase, forearm). The changing of amplitude for different frequencies over time is shown. The amplitudes are depicted in different colors: warmer colors represent higher amplitudes. B: time-averaged wavelet power spectrum of the same signal. Frequency axis on A and B is presented in a logarithmic scale to allow for a reliable resolution of the low-frequency components. PU, perfusion units.

Figure 2.

A representative tracing of the raw laser-Doppler signal of skin microcirculation obtained at the finger pulp and the volar forearm during baseline phase (A) and exercise and recovery phase (B). Scale on A and B is different as we wanted to show the typical oscillations of the signal that appear also in the baseline state but on a smaller scale. PU, perfusion units.

Figure 3.

An example of a time-averaged wavelet power spectra of a laser-Doppler signal obtained during the baseline phase (blue line) and the early recovery phase (red line) after dynamic exercise on finger pulp (A) and volar forearm (B). Frequencies corresponding to endothelial nitric oxide-dependent (endo NO), neurogenic (neuro), myogenic (myo), respiratory (resp), and cardiac (card) physiological influences were detected. PU, perfusion units.

Figure 4.

Relative power (the ratio between the median power within a given frequency interval and the median power of the overall power spectrum) in the frequency spectrum of the laser-Doppler perfusion signal acquired in 21 men and 6 women during baseline (blue) and the early recovery phase (red) in finger pulp (A) and volar forearm (B). Horizontal lines in the box indicate the first quartile, median, and third quartile. The whiskers represent one and a half times the interquartile range. The outlines are represented as points. *P < 0.05, statistically significant differences between baseline and early recovery (Wilcoxon signed-rank test). Endothelial nitric oxide-dependent (P = 0.005) and respiratory (P = 0.001) components were significantly increased, and the myogenic component (P = 0.02) significantly decreased during early recovery in the finger pulp. In the forearm, only the RP of the endothelial nitric oxide-dependent (P = 0.009) component was significantly increased. card, cardiac; endo NO, endothelial nitric oxide dependent; neuro, neurogenic; myo, myogenic; resp, respiratory.