Approximate entropy normalized measures for analyzing social neurobiological systems

FONSECA, Sofia, MILHO, João, PASSOS, Pedro, ARAÚJO, Duarte and DAVIDS, Keith (2012). Approximate entropy normalized measures for analyzing social neurobiological systems. Journal of Motor Behavior, 44 (3), 179-183.

Full text not available from this repository.
Official URL:
Link to published version::


When considering time series data of variables describing agent interactions in social neurobiological systems, measures of regularity can provide a global understanding of such system behaviors. Approximate entropy (ApEn) was introduced as a nonlinear measure to assess the complexity of a system behavior by quantifying the regularity of the generated time series. However, ApEn is not reliable when assessing and comparing the regularity of data series with short or inconsistent lengths, which often occur in studies of social neurobiological systems, particularly in dyadic human movement systems. Here, the authors present two normalized, nonmodified measures of regularity derived from the original ApEn, which are less dependent on time series length. The validity of the suggested measures was tested in well-established series (random and sine) prior to their empirical application, describing the dyadic behavior of athletes in team games. The authors consider one of the ApEn normalized measures to generate the 95th percentile envelopes that can be used to test whether a particular social neurobiological system is highly complex (i.e., generates highly unpredictable time series). Results demonstrated that suggested measures may be considered as valid instruments for measuring and comparing complexity in systems that produce time series with inconsistent lengths.

Item Type: Article
Research Institute, Centre or Group - Does NOT include content added after October 2018: Centre for Sports Engineering Research
Identification Number:
Page Range: 179-183
Depositing User: Carole Harris
Date Deposited: 01 Oct 2013 11:31
Last Modified: 18 Mar 2021 19:45

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics