Multiscale entropy analysis of biological signals: a fundamental bi-scaling law.

Multiscale entropy analysis of biological signals: a fundamental bi-scaling law.
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DOI:
10.3389/fncom.2015.00064
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发表时间:
2015
影响因子:
3.2
通讯作者:
Cao Y
Cao Y
中科院分区:
医学4区
文献类型:
--
作者:
Gao J;Hu J;Liu F;Cao Y

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自2000年初引入多尺度熵(MSE)以来,它在生物信号分析中得到了广泛的应用,并扩展到了多元MSE。然而,到目前为止,还没有关于MSE或多变量MSE的分析结果的报道。这严重限制了我们对MSE的基本理解。例如,使用缺省参数值和短数据集估计的均方误差是否有意义还没有研究。也不知道MSE是否与其他复杂性度量有任何关系,例如表征数据相关性结构的赫斯特参数。为了克服这一局限,更重要的是,为了指导MSE在生命科学的各个领域中更有成效的应用,我们推导了一个关于分形时间序列的基本双标度定律,一个是关于相空间的尺度,另一个是关于用于平滑的块大小。我们通过检验两种类型的生理数据来说明该方法的有效性。一种是心率变异性(HRV)数据,目的是区分健康受试者和充血性心力衰竭患者,充血性心力衰竭是一种危及生命的疾病。另一种是脑电(EEG)数据,用于区分癫痫发作脑电和正常健康脑电。
Since introduced in early 2000, multiscale entropy (MSE) has found many applications in biosignal analysis, and been extended to multivariate MSE. So far, however, no analytic results for MSE or multivariate MSE have been reported. This has severely limited our basic understanding of MSE. For example, it has not been studied whether MSE estimated using default parameter values and short data set is meaningful or not. Nor is it known whether MSE has any relation with other complexity measures, such as the Hurst parameter, which characterizes the correlation structure of the data. To overcome this limitation, and more importantly, to guide more fruitful applications of MSE in various areas of life sciences, we derive a fundamental bi-scaling law for fractal time series, one for the scale in phase space, the other for the block size used for smoothing. We illustrate the usefulness of the approach by examining two types of physiological data. One is heart rate variability (HRV) data, for the purpose of distinguishing healthy subjects from patients with congestive heart failure, a life-threatening condition. The other is electroencephalogram (EEG) data, for the purpose of distinguishing epileptic seizure EEG from normal healthy EEG.
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