Approximate entropy (ApEn) is a recently developed formula to quantify the amount of regularity in data. We examine the potential applicability of ApEn to clinical endocrinology to quantify pulsatility in hormone secretion data. We evaluate the role of ApEn as a complementary statistic to widely employed pulse-detection algorithms, represented herein by ULTRA, via the analysis of two different classes of models that generate episodic data. We conclude that ApEn is able to discern subtle system changes and to provide insights separate from those given by ULTRA. ApEn evaluates subordinate as well as peak behavior and often provides a direct measure of feedback between subsystems. ApEn generally can distinguish systems given 180 data points and an intra-assay coefficient of variation of 8%. This suggests ApEn as applicable to clinical hormone secretion data within the foreseeable future. Additionally, the models analyzed and extant clinical data are both consistent with episodic, not periodic, normative physiology.