DispEn: Dispersion Entropy

In jcaude/MSEntropy: MultiScale Dispersion Entropy Calculations

Description

This function calculates dispersion entropy of a univariate signal x, using different mapping approaches (MA)

Usage

DispEn(x, ma = "NCDF", m = 3, nc = 6, tau = 1, mu, sigma)

Arguments

x	a univariate signal (vector)
ma	the mapping approach, possible values are: LM: linear mapping NCDF: normal cumulative distribution function (default) LOGSIG: logarithm sigmoid TANSIG: tangent sigmoid SORT: sorting method
m	the embedding dimension
nc	the number of classes (it is usually equal to a number between 3 and 9, we use 6 by default)
tau	the time lag (it is usually equal to 1)
mu	an optional defined mean (the observed mean by default)
sigma	an option defined std-deviation (the observed std-deviation by default)

Value

a named list:

• disp.en: the Dispersion Entropy value of the signal x

• pdf: a vector of length nc^m, showing the normalized number of disersion patterns of x

References

1. Azami H, Entropy JE, (2018) "Amplitude-and Fluctuation-Based Dispersion Entropy". Entropy 20:210.

2. Rostaghi M, Letters HAISP, (2016) "Dispersion entropy: A measure for time-series analysis". IEEE Signal Processing Letters. vol. 23, n. 5, pp. 610-614, 2016.

Examples

data(EG_181117)
DispEn(EG_181117,m=1)

jcaude/MSEntropy documentation built on May 21, 2021, 7:31 p.m.