Physiological time-series analysis: what does regularity quantify?

From SUDEP Wiki
Revision as of 08:34, 17 October 2017 by Ycarmen1 (talk | contribs) (Created page with "''Pincus SM and Goldberger AL (1994) Physiological time-series analysis: what does regularity quantify? Am J Physiol 266:4 Pt 2 H1643–56.'' '''[http://ajpheart.physiology.o...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

Pincus SM and Goldberger AL (1994) Physiological time-series analysis: what does regularity quantify? Am J Physiol 266:4 Pt 2 H1643–56.

Link to Article

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 entropy, complexity, chaos, stochastic processes, nonlinear dynamics, heart rate variability

Context

Comments