The appropriate use of approximate entropy and sample entropy with short data sets.
The appropriate use of approximate entropy and sample entropy with short data sets.
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DOI：
10.1007/s10439-012-0668-3
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发表时间：
2013-03
影响因子：
3.8
通讯作者：
Stergiou N
中科院分区：
文献类型：
--
作者：
Yentes JM;Hunt N;Schmid KK;Kaipust JP;McGrath D;Stergiou N
近似熵（ApEn）和样本熵（SampEn）是用于测量时间序列内的可重复性或可预测性的数学算法。这两种算法对它们的输入参数都非常敏感：m（被比较的数据段的长度），r（相似性标准）和N（数据长度）。在短数据集，特别是生物数据的参数选择上，还没有建立共识。因此，本研究的目的是通过探索参数值变化对短数据集的影响来检验这两种熵算法的鲁棒性。利用了具有已知理论熵质量的数据以及来自健康年轻人和老年人的实验数据。我们的研究结果表明，ApEn和SampEn都对参数选择非常敏感，特别是对于N≤200的非常短的数据集。我们建议使用N大于200，m等于2，并在选择参数之前检查几个r值。在使用这两种算法选择实验研究参数时应极为谨慎。根据我们目前的发现，SampEn对于短数据集似乎更可靠。SampEn对数据长度的变化不太敏感，并且在相对一致性方面的问题较少。
Approximate entropy (ApEn) and sample entropy (SampEn) are mathematical algorithms created to measure the repeatability or predictability within a time series. Both algorithms are extremely sensitive to their input parameters: m (length of the data segment being compared), r (similarity criterion) and N (length of data). There is no established consensus on parameter selection in short data sets, especially for biological data. Therefore, the purpose of this research was to examine the robustness of these two entropy algorithms by exploring the effect of changing parameter values on short data sets. Data with known theoretical entropy qualities as well as experimental data from both healthy young and older adults was utilized. Our results demonstrate that both ApEn and SampEn are extremely sensitive to parameter choices, especially for very short data sets, N≤200. We suggest using N larger than 200, an m of 2 and examine several r values before selecting your parameters. Extreme caution should be used when choosing parameters for experimental studies with both algorithms. Based on our current findings, it appears that SampEn is more reliable for short data sets. SampEn was less sensitive to changes in data length and demonstrated fewer problems with relative consistency.
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影响因子：
2.1
作者：
Deffeyes, Joan E.;Harbourne, Regina T.;Stuberg, Wayne A.;Stergiou, Nicholas
通讯作者：
Stergiou, Nicholas
影响因子：
5.1
作者：
Lamoth CJ;van Deudekom FJ;van Campen JP;Appels BA;de Vries OJ;Pijnappels M
通讯作者：
Pijnappels M
影响因子：
2.9
作者：
Hong, S. Lee;Newell, Karl M.
通讯作者：
Newell, Karl M.
影响因子：
2.2
作者：
Georgoulis, Anastasios D;Moraiti, Constantina;Stergiou, Nicholas
通讯作者：
Stergiou, Nicholas
影响因子：
3.2
作者：
Liu, Chengyu;Liu, Changchun;Liu, Feng
通讯作者：
Liu, Feng