Example 6: Multiscale [Increment] Entropy

Import a signal of uniformly distributed pseudorandom integers in the range [1,8] and create a multiscale entropy object with the following parameters:

EnType = IncrEn(), embedding dimension = 3, a quantifying resolution = 6, normalization = true.

X = EH.ExampleData('randintegers');

Mobj = EH.MSobject('IncrEn', m = 3, R = 6, Norm = True)

Mobj.Func
>>> <function EntropyHub._IncrEn.IncrEn(Sig, m=2, tau=1, R=4, Logx=2, Norm=False)>

Mobj.Kwargs
>>> {'m': 3, 'R': 6, 'Norm': True}

Calculate the multiscale increment entropy over 5 temporal scales using the modified graining procedure where,

\[y_j^{(\tau)} =\frac{1}{\tau } \sum_{i=\left(j-1\right)\tau +1}^{j\tau }x{_i} , 1 <= j <= \frac{N}{\tau}\]

MSx, _ = EH.MSEn(X, Mobj, Scales = 5, Methodx = 'modified')
. . . . . .

>>> MSx =
    array([4.2719 4.3059 4.2863 4.2494 4.2773])