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.