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Quantification of hormone pulsatility via an approximate entropy algorithm.

S M Pincus¹, D L Keefe

¹Department of Obstetrics and Gynecology, Yale University School of Medicine, New Haven, Connecticut 06510.

The American Journal of Physiology

|May 1, 1992

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Approximate entropy (ApEn) quantifies data regularity and shows promise for analyzing hormone secretion pulsatility in endocrinology. It offers insights beyond traditional pulse-detection algorithms, aiding in understanding subtle system changes.

Area of Science:

Endocrinology
Biomedical Engineering
Data Analysis

Background:

Hormone secretion exhibits complex pulsatility, crucial for physiological regulation.
Existing pulse-detection algorithms may not capture all aspects of secretory dynamics.

Purpose of the Study:

To evaluate the applicability of Approximate Entropy (ApEn) for quantifying hormone secretion pulsatility in clinical endocrinology.
To assess ApEn as a complementary tool to established pulse-detection algorithms like ULTRA.

Main Methods:

Analysis of two distinct models generating episodic data.
Comparison of ApEn's performance against the ULTRA algorithm.
Evaluation of data requirements (180 data points) and assay variability (8% CV).

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Main Results:

ApEn effectively discerns subtle system changes and provides insights distinct from ULTRA.
ApEn quantifies subordinate and peak behaviors, offering measures of subsystem feedback.
ApEn demonstrates capability with 180 data points and 8% intra-assay coefficient of variation.

Conclusions:

ApEn is a valuable tool for analyzing hormone secretion pulsatility in clinical endocrinology.
ApEn offers complementary information to existing methods, enhancing the understanding of endocrine feedback loops.
Normative physiology is characterized by episodic, rather than periodic, hormone secretion.