Physiological measurements are crucial for monitoring human health and well-being. One of the most promising methods for such measurements is remote photoplethysmography (rPPG), a non-contact optical method that uses a digital camera to detect subtle blood volume changes in skin microcirculation. Two key challenges in rPPG relate to ensuring the robust performance of the rPPG algorithms and, upon a thorough statistical evaluation, validating the physiological parameters extracted from the rPPG pulse waveform signals. In part one of our research, an application of a wavelet transform is proposed to decompose the colour signals extracted from facial video recordings to increase the rPPG signals' dimensionality for the purpose of measuring the pulse rate (PR). In part two, we assess the validity of the rPPG-derived, ultra-short-term pulse rate variability (UST-PRV) metrics (SDNN, RMSSD, pNN50) in a sufficiently rigorous manner. The results show that the algorithm applying the proposed decomposition approach outperforms the state-of-the-art Sub-Band rPPG (SB) algorithm in terms of signal-to-noise ratio and the level of agreement between the measured and reference PRs. The results of UST-PRV analysis show that significant correlation, non-bias, and statistical significance are only obtained for SDNN, partially confirming the validity of the rPPG-derived UST-PRV metrics.