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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Sample entropy ： ウィキペディア英語版 Sample entropy Sample entropy (SampEn) is a modification of approximate entropy (ApEn), used extensively for assessing the complexity of a physiological time-series signal, thereby diagnosing diseased state. Unlike ApEn, SampEn shows good traits such as data length independence and trouble-free implementation. Also, there is a small computational difference: In ApEn, the comparison between the template vector (see below) and the rest of the vectors also includes comparison with itself. This guarantees that probabilities $C\_\text{'}^(r)$ are never zero. Consequently, it is always possible to take a logarithm of probabilities. Because template comparisons with itself lower ApEn values, the signals are interpreted to be more regular than they actually are. These self-matches are not included in SampEn. There is a multiscale version of SampEn as well, suggested by Costa and others. == Definition == Like approximate entropy (ApEn), Sample entropy (SampEn) is a measure of complexity (). But it does not include self-similar patterns as ApEn does. For a given embedding dimension $m$, tolerance $r$ and number of data points $N$, SampEn is the negative logarithm of the probability that if two sets of simultaneous data points of length $m$ have distance then two sets of simultaneous data points of length $m+1$ also have distance . And we represent it by $SampEn(m,r,N)$ (or by $SampEn(m,r,\backslash tau,N)$ including sampling time $\backslash tau$). Now assume we have a time-series data set of length $N\; =$ with a constant time interval $\backslash tau$. We define a template vector of length $m$, such that $X\_m\; (i)=\; ,\; .\; .\; .\; ,\; x\_\; \backslash \}\; \}$ and the distance function $d()$ (i≠j) is to be the Chebyshev distance (but it could be any distance function, including Euclidean distance). We count the number of vector pairs in template vectors of length $m$ and $m+1$ having and denote it by $B$ and $A$ respectively. We define the sample entropy to be :$$ SampEn=-\log Where, $A$ = no of template vector pairs having of length $m+1$ $B$ = no of template vector pairs having of length $m$ It is clear from the definition that $A$ will always have a value smaller or equal to $B$. Therefore, $SampEn(m,r,\backslash tau)$ will be always either be zero or positive value. A smaller value of $SampEn$ also indicates more self-similarity in data set or less noise. Generally we take the value of $m$ to be $2$ and the value of $r$ to be $0.2\; \backslash times\; std$. Where std stands for standard deviation which should be taken over a very large dataset. For instance, the r value of 6 ms is appropriate for sample entropy calculations of heart rate intervals, since this corresponds to $0.2\; \backslash times\; std$ for a very large population. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Sample entropy」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.