## 1 Introduction  
Laser Doppler fluxmetry (LDF) has long been considered the gold standard for non-invasive assessment of skin microvascular function [1, 2]. Spectral analysis has been shown to provide a much better insight into the characteristics of the measured laser Doppler (LD) signal than a mere analysis of the arbitrary LD flux values (presumably reflecting blood flow values) over time by decomposing them into their basic frequency spectra. Six frequency intervals have usually been described, related to various physiological influences (endothelial nitric oxide (NO)-independent (0.005-0.0095 Hz), endothelial NO-dependent (0.0095-0.021 Hz), neurogenic (0.021-0.052 Hz), myogenic (0.052-0.15 Hz), respiratory (0.15-0.4 Hz), and cardiac (0.4-2.0 Hz)) modulating the microcirculatory response [3]. Wavelet analysis (WA) is a perspective spectral analysis technique that enables refined spectral reconstruction of complex, non-stationary, and non-linear microcirculatory signals. The advantages of WA over the gold-standard Fourier transform (FT) have been described in many studies. Nevertheless, WA is not yet standardized [4], and its use is limited to custom-developed tools. While the physical principles underlying the basic WA algorithm are well-described [4], some questions remain. Since WA is mostly used on a finite-length time series, the results inevitably suffer from the edge effects that lead to distortions of the spectral amplitude. The cone of influence (COI) (Figure 1B,C) delineates regions of the wavelet spectrum where these edge effects become important, separating the accurately computed wavelet coefficients from those that are inaccurate due to the wavelet extending beyond the ends of the signal [5; 6]. As the COI reduces the available data portion at both ends of the signal, these regions may become critical for short-duration recordings, especially at low frequencies [7]. The boundaries of the COI regions are usually defined as the e-folding time for the autocorrelation of the wavelet transform (WT) at each scale [5; 8]; but other methods for approximating them have also been proposed, including defining the extent of the COI at each scale as the point at which the magnitude of the WT drops to 2% of its peak value [6] or adding and subtracting half of the wavelet footprint, defined as the time interval encompassing approximately 95% of the wavelet's energy at the beginning and end of the observation interval at each scale [9; 10]. Despite the widespread use of WA for the evaluation of LD signals, and the fact that the COI-related edge effects are well-known in various WA applications [5; 6; 11; 12; 13], the question of to what extent performing WA by taking the values outside the COI affects the final analysed results has not yet been fully investigated. The approaches used by different study groups differ: while some explicitly state that they performed WA only on data unaffected by the COI-related edge effects [7]; others do not consider the COI-related edge effects. Due to the lack of similar corresponding studies, we believe that the importance of COI is underscored in evaluating biological signals despite the routinely used WA as an established tool to assess microcirculatory dynamics. Since LD signals are complicated, it is important to perform the spectral analysis as accurately as possible, especially when considering low-frequency intervals related to the influence of the vascular endothelium, which is generally difficult to assess [4; 14]. Considering that incorporating the COI correction may assure better validity of the WA results, addressing its impact is relevant. LD signals naturally exhibit a steady-state behaviour with periodic fluctuations around a stable mean. However, several common provocations exist that induce a transient response in the signal, such as post-occlusive reactive hyperemia (PORH), local heating, and iontophoresis of a vasodilatory substance.