Example 8: Composite Multiscale Cross-[Approximate] Entropy
Import two signals of uniformly distributed pseudorandom integers in the range [1 8] and create a multiscale entropy object with the following parameters:
EnType = XApEn()
, embedding dimension = 2, time delay = 2, radius distance threshold = 0.5.
X = EH.ExampleData('randintegers2');
Mobj = EH.MSobject('XApEn', m = 2, tau = 2, r = 0.5)
Mobj.Func
>>> <function EntropyHub._XApEn.XApEn(Sig, m=2, tau=1, r=None, Logx=2.71828)>
Mobj.Kwargs
>>> {'m': 2, 'tau': 2, 'r': 0.5}
Calculate the comsposite multiscale cross-approximate entropy over 3 temporal scales
where the radius distance threshold value (r
) specified by Mobj
becomes scaled by the
variance of the signal at each scale.
MSx, _ = EH.cXMSEn(X[:,0], X[:,1], Mobj, Scales = 3, RadNew = 1)
. . . . . . .
>>> MSx =
array([1.089, 1.4746, 1.2932])