Several approaches for estimating complexity in physiological time series at various time scales have recently been developed, with a special focus on heart rate variability (HRV) series. While numerous multiscale complexity quantifiers have been investigated, a multiscale Kolmogorov-Sinai (K-S) entropy for the characterization of cardiovascular dynamics still has to be properly assessed. In this pilot study, we investigate the Algorithmic Information Content, which is calculated using an effective compression algorithm, to quantify multiscale partition- based K-S entropy on experimental HRV series. Data were gathered from publicly available datasets comprising long-term, unstructured recordings from 10 healthy subjects, as well as 10 patients with congestive heart failure (CHF) and 10 patients with atrial fibrillation. Results show that multiple time scales and domain partitions statistically discern healthy vs. pathological cardiovascular dynamics. We conclude that the proposed multiscale partition-based K-S entropy may constitute a viable tool for the complexity assessment of cardiovascular variability series.