Example 7: Refined Multiscale [Sample] EntropyΒΆ

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

EnType = SampEn(), embedding dimension = 4, radius threshold = 1.25

X = EH.ExampleData('randintegers');

Mobj = EH.MSobject('SampEn', m = 4, r = 1.25)

Mobj.Func
>>> <function EntropyHub._SampEn.SampEn(Sig, m=2, tau=1, r=None, Logx=2.71828)>

Mobj.Kwargs
>>> {'m': 4, 'r': 1.25}

Calculate the refined multiscale sample entropy and the complexity index (Ci) over 5 temporal scales using a 3rd order Butterworth filter with a normalised corner frequency of at each temporal scale, where the radius threshold value (r) specified by Mobj becomes scaled by the median absolute deviation of the filtered signal at each scale.

MSx, Ci = EH.rMSEn(X, Mobj, Scales = 5, F_Order = 3, F_Num = 0.6, RadNew = 4)
. . . . . .

>>> MSx
    array([0.5280, 0.5734, 0.5940, 0.5908, 0.5564])
>>> Ci
    2.842518179468045