Orderliness of hormone release.
ApEn, approximate entropy, is a recently formulated family of parameters and statistics quantifying regularity (orderliness) in serial data, with developments both within theoretical mathematics, as well as numerous applications to multiple biological contexts. ApEn appears to have broad application to hormone pulsatility analysis within endocrinology, bringing a new perspective to the assessment of secretory patterns. ApEn is complementary to pulse detection algorithms widely employed to evaluate hormone secretion time-series--it is scale-invariant and model-independent, evaluates both dominant and subordinant patterns in data, discriminates series for which clear pulse recognition is difficult, and often provides a direct barometer of feedback between subsystems. ApEn is applicable to systems with at least 50 data points and to broad classes of models: it can be applied to discriminate both general classes of correlated stochastic processes, as well as noisy deterministic systems. Moreover, ApEn is complementary to spectral and autocorrelation analyses, providing effective discriminatory capability in instances in which the aforementioned measures exhibit minimal distinctions. We present some basic background on the above, and illustrate various facets of ApEn utility via several representative endocrinological studies.
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D000818:Animals; D006728:Hormones / Q000378:metabolism*; D006801:Humans; D008432:Mathematical Computing
D006728:Hormones