Sample entropy

Sample entropy (SampEn) is a modification of approximate entropy (ApEn), used for assessing the complexity of physiological time-series signals, diagnosing diseased states.^[1] 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 $C_{{i}}'^{{m}}(r)$ 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.

There is a multiscale version of SampEn as well, suggested by Costa and others.^[2]

Definition

Like approximate entropy (ApEn), Sample entropy (SampEn) is a measure of complexity.^[1] But it does not include self-similar patterns as ApEn does. For a given embedding dimension $m$, tolerance $r$ and number of data points $N$, SampEn is the negative logarithm of the probability that if two sets of simultaneous data points of length $m$ have distance $<r$ then two sets of simultaneous data points of length $m+1$ also have distance $<r$. And we represent it by $SampEn(m,r,N)$ (or by $SampEn(m,r,\tau ,N)$ including sampling time $\tau$).

Now assume we have a time-series data set of length $N={\{x_{1},x_{2},x_{3},...,x_{N}\}}$ with a constant time interval $\tau$. We define a template vector of length $m$, such that $X_{m}(i)={\{x_{i},x_{{i+1}},x_{{i+2}},...,x_{{i+m-1}}\}}$ and the distance function $d[X_{m}(i),X_{m}(j)]$ (i≠j) is to be the Chebyshev distance (but it could be any distance function, including Euclidean distance). We count the number of vector pairs in template vectors of length $m$ and $m+1$ having $d[X_{m}(i),X_{m}(j)]<r$ and denote it by $B$ and $A$ respectively. We define the sample entropy to be

$SampEn=-\log {A \over B}$

Where

$A$ = number of template vector pairs having $d[X_{{m+1}}(i),X_{{m+1}}(j)]<r$ of length $m+1$

$B$ = number of template vector pairs having $d[X_{m}(i),X_{m}(j)]<r$ of length $m$

It is clear from the definition that $A$ will always have a value smaller or equal to $B$. Therefore, $SampEn(m,r,\tau )$ will be always either be zero or positive value. A smaller value of $SampEn$ also indicates more self-similarity in data set or less noise.

Generally we take the value of $m$ to be $2$ and the value of $r$ to be $0.2\times std$. Where std stands for standard deviation which should be taken over a very large dataset. For instance, the r value of 6 ms is appropriate for sample entropy calculations of heart rate intervals, since this corresponds to $0.2\times std$ for a very large population.

Multiscale SampEn

The definition mentioned above is a special case of multi scale sampEn with $\delta =1$,where $\delta$ is called skipping parameter. In multiscale SampEn template vectors are defined with a certain interval between its each element specified by the value of $\delta$. And modified template vector is defined as $X_{{m,\delta }}(i)={x_{i},x_{{i+\delta }},x_{{i+2\times \delta }},...,x_{{i+(m-1)\times \delta }}}$ and sampEn can be written as $SampEn\left(m,r,\delta \right)=-\log {A_{\delta } \over B_{\delta }}$ And we calculate $A_{\delta }$ and $B_{\delta }$ like before.

Implementation

Sample entropy can be implemented easily in many different programming languages. An example written in Matlab can be found here. An example written for R can be found here.

References

1. Richman, JS; Moorman, JR (2000). "Physiological time-series analysis using approximate entropy and sample entropy". American Journal of Physiology. Heart and Circulatory Physiology. 278 (6): H2039–49. PMID 10843903.
2. Costa, Madalena; Goldberger, Ary; Peng, C.-K. (2005). "Multiscale entropy analysis of biological signals". Physical Review E. 71 (2). doi:10.1103/PhysRevE.71.021906.