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Approximate entropy as a measure of irregularity for psychiatric serial metrics

Authors


  • The author of this paper does not have any commercial associations that might pose a conflict of interest in connection with this manuscript.

Steven M Pincus, 990 Moose Hill Road, Guilford, CT 06437, USA. Fax: +1 203 453 8969; e-mail: stevepincus@alum.mit.edu

Abstract

Objectives:  The quantification of subtle patterns in sequential data, and their changes, has considerable potential utility throughout psychiatry, including the analyses of mood ratings, heart rate, respiratory, and electroencephalographic recordings.

Methods:  Approximate entropy (ApEn), a relatively recently developed statistic quantifying serial irregularity, has been applied in numerous studies throughout mathematics and other fields of study, especially biology.

Results:  We discussed applications of ApEn, both extant and potential, of most relevance to psychiatrists. We provided a mechanistic interpretation of lowered ApEn values, and discusses the relationship between ApEn and other (both classical and complexity) measures of serial dynamics. We also briefly discussed cross-ApEn, a thematically similar quantification of two-variable asynchrony that can aid in uncovering subtle disruptions in complicated network dynamics.

Conclusions:  ApEn and cross-ApEn have significant potential to consequentially enhance present statistical methodologies of analysis of psychiatric data, in both clinical and in research settings.

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