Sample Entropy (SampEn)

Estimates the complexity of a time series using sample entropy (Richman & Moorman 2000): the negative log ratio of template matches of length m + 1 to template matches of length m, using a fixed Chebyshev-distance tolerance. Direct C++ port of the counting core in PhysioNet's reference mse.c (Costa), validated to reproduce the compiled reference binary's output exactly (to its own displayed precision) on synthetic test data. See inst/COPYRIGHTS.

Usage

sample_entropy(x, m = 2L, r = 0.15)

Arguments

x

Numeric vector. The time series to analyse.

m

Integer >= 1. Template length. Default 2 (the standard MSE convention, per Costa et al.).

r

Numeric > 0. Tolerance, as a fraction of x's own standard deviation (i.e. the actual Chebyshev-distance tolerance used is sd(x) * r). Default 0.15 (the standard MSE convention).

Value

A length-1 numeric: the sample entropy. If there are too few valid template pairs to compare (length(x) - m < 2), returns NA. If there are zero matches at either template length (undefined ratio), returns the conventional fallback -log(1 / (N*(N-1))) used by the reference implementation, where N = length(x) - m.

Details

This is also the building block multiscale_entropy() applies at each coarse-grained scale – see R/multiscale.R.

References

Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000;278(6):H2039-H2049.

Examples

set.seed(1)
sample_entropy(rnorm(1000))
#> [1] 2.450377