Sudden drops in pulse wave amplitude (PWA) measured by finger photoplethysmography (PPG) are known to reflect peripheral vasoconstriction resulting from sympathetic activation. Previous work demonstrated that sympathetic activations during sleep typically accompany the occurrence of pathological respiratory and motor events, and their alteration may be associated with the arising of metabolic and cardiovascular diseases. Importantly, PWA-drops often occur in the absence of visually identifiable cortical micro-arousals and may thus represent a more accurate marker of sleep disruption/fragmentation. In this light, an objective and reproducible quantification and characterization of sleep-related PWA-drops may offer a valuable, non-invasive approach for the diagnostic and prognostic evaluation of patients with sleep disorders. However, the manual identification of PWA-drops represents a time-consuming practice potentially associated with high intra/inter-scorer variability. Since validated algorithms are not readily available for research and clinical purposes, here we present a novel automated approach to detect and characterize significant drops in the PWA-signal. The algorithm was tested against expert human scorers who visually inspected corresponding PPG-recordings. Results demonstrated that the algorithm reliably detects PWA-drops and is able to characterize them in terms of parameters with a potential physiological and clinical relevance, including timing, amplitude, duration and slopes. The method is completely user-independent, processes all-night PSG-data, automatically dealing with potential artefacts, sensor loss/displacements, and stage-dependent variability in PWA-time-series. Such characteristics make this method a valuable candidate for the comparative investigation of large clinical datasets, to gain a better insight into the reciprocal links between sympathetic activity, sleep-related alterations, and metabolic and cardiovascular diseases.