Abstract
Approximate entropy (ApEn) is a recently developed statistic quantifying regularity and complexity that appears to have potential application to a wide variety of physiological and clinical time-series data. The focus here is to provide a better understanding of ApEn to facilitate its proper utilization, application, and interpretation. After giving the formal mathematical description of ApEn, we provide a multistep description of the algorithm as applied to two contrasting clinical heart rate data sets. We discuss algorithm implementation and interpretation and introduce a general mathematical hypothesis of the dynamics of a wide class of diseases, indicating the utility of ApEn to test this hypothesis. We indicate the relationship of ApEn to variability measures, the Fourier spectrum, and algorithms motivated by study of chaotic dynamics. We discuss further mathematical properties of ApEn, including the choice of input parameters, statistical issues, and modeling considerations, and we conclude with a section on caveats to ensure correct ApEn utilization.
Keywords
Approximate entropyComputer scienceStatisticEntropy (arrow of time)Time seriesStatisticsAlgorithmMathematicsMachine learning
Affiliated Institutions
Related Publications
Approximate entropy (ApEn) as a complexity measure
Approximate entropy (ApEn) is a recently developed statistic quantifying regularity and complexity, which appears to have potential application to a wide variety of relatively s...
Heart rate control in normal and aborted-SIDS infants
Approximate entropy (ApEn), a mathematical formula quantifying regularity in data, was applied to heart rate data from normal and aborted-sudden infant death syndrome (SIDS) inf...
Physiological time-series analysis using approximate entropy and sample entropy
Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, howev...
Entropy measures of heart rate variation in conscious dogs
Our goal was to determine the contributions of sympathetic and parasympathetic activity to entropy measures of heart rate variability (HRV). We compared our results with two com...
Effects of head-down bed rest on complex heart rate variability: response to LBNP testing
Head-down bed rest is used to model physiological changes during spaceflight. We postulated that bed rest would decrease the degree of complex physiological heart rate variabili...
Publication Info
- Year
- 1994
- Type
- article
- Volume
- 266
- Issue
- 4
- Pages
- H1643-H1656
- Citations
- 1313
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1313
OpenAlex
Cite This
Steve Pincus,
Ary L. Goldberger
(1994).
Physiological time-series analysis: what does regularity quantify?.
American Journal of Physiology-Heart and Circulatory Physiology
, 266
(4)
, H1643-H1656.
https://doi.org/10.1152/ajpheart.1994.266.4.h1643
Identifiers
- DOI
- 10.1152/ajpheart.1994.266.4.h1643