Sample entropy

Sample entropy (SampEn; more appropriately K_2 entropy or Takens-Grassberger-Procaccia correlation entropy ) is a modification of <a href="/facts/Approximate_entropy/lsBcewKh">approximate entropy</a> (ApEn; more appropriately "Procaccia-Cohen entropy"), used for assessing the complexity of physiological and other <a href="/facts/Time-series/fSXPR817">time-series</a> signals, diagnosing e.g. diseased states. SampEn has two advantages over ApEn: data length independence and a relatively trouble-free implementation. Also, there is a small computational difference: In ApEn, the comparison between the template vector (see below) and the rest of the vectors also includes comparison with itself. This guarantees that probabilities 
 
 
 
 
 C
 
 i
 
 
 ′
 
 m
 
 
 
 (
 r
 )
 
 
 {\displaystyle C_{i}'^{m}(r)}
 
 are never zero. Consequently, it is always possible to take a logarithm of probabilities. Because template comparisons with itself lower ApEn values, the signals are interpreted to be more regular than they actually are. These self-matches are not included in SampEn. However, since SampEn makes direct use of the correlation integrals, it is not a real measure of information but an approximation. The foundations and differences with ApEn, as well as a step-by-step tutorial for its application is available at.
SampEn is indeed identical to the "correlation entropy" K_2 of Grassberger & Procaccia, except that it is suggested in the latter that certain limits should be taken in order to achieve a result invariant under changes of variables. No such limits and no invariance properties are considered in SampEn.
There is a multiscale version of SampEn as well, suggested by Costa and others. SampEn can be used in biomedical and biomechanical research, for example to evaluate postural control.

Sample entropy open-in-new

Sample entropy