Sample entropy quantifies the irregularity of a time series as the negative
natural log of the conditional probability that sequences similar for m
points remain similar at the next point, within tolerance r (self-matches
excluded). Larger values indicate greater irregularity/complexity; a perfectly
regular signal tends to zero.
Usage
sampleEntropy(x, m = 2L, r = 0.2, normalize = TRUE)
Arguments
- x
Numeric time series.
- m
Embedding (template) length (default 2).
- r
Similarity tolerance. If normalize = TRUE (default) it is a
multiple of the series standard deviation (default 0.2).
- normalize
If TRUE, r is scaled by sd(x).
Value
A single numeric sample-entropy value (Inf if no length-m+1
matches occur).
References
Richman JS, Moorman JR (2000). Am J Physiol 278(6):H2039-H2049.
Examples
sampleEntropy(sin(seq(0, 40 * pi, length.out = 800))) # ~0 (regular)
#> [1] 0.2275373