取样熵软件

A bearable and vectorized implementation of Sample Entropy (SampEn).

软件应用简介

This function computes the Sample Entropy (SampEn) algorithm according to the Richman, J. S., & Moorman, J. R. (2000) recommendations. The script is bearable, compressed and vectorized. Therefore, the computation cost is minimal.

Furthermore, extraordinary cases when SampEn is not defined are considered: 

– If B = 0, no regularity has been detected. A common SampEn implementation would return -Inf value. 

– If A = 0, the conditional probability is zero (A/B = 0), returning an Inf value.

According to Richman & Moorman, the upper bound of SampEn must be A/B = 2/[(N-m-1)(N-m)], returning SampEn = log(N-m)+log(N-m-1)-log(2). Hence, whenever A or B are equal to 0, that is the correct value.

Input parameters: 

– signal: Signal vector with dims. [1xN] 

– m: Embedding dimension (m < N). 

– r: Tolerance (percentage applied to the SD). 

– dist_type: (Optional) Distance type, specified by a string. Default value: ‘chebychev’ (type help pdist for further information).

Output variables: 

– value: SampEn value.

Example of use: 

signal = rand(200,1); 

value = sampen(signal,1,0.2)

界面展示

结果示意

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