Courtiol Julie, Perdikis Dionysios, Petkoski Spase, Müller Viktor, Huys Raoul, Sleimen-Malkoun Rita, Jirsa Viktor K
Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, 27 Bd Jean Moulin, 13385 Marseille, France.
Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, 27 Bd Jean Moulin, 13385 Marseille, France; Aix Marseille Univ, CNRS, ISM, Institut des Sciences du Mouvement, 163 Av de Luminy, 13288 Marseille, France.
J Neurosci Methods. 2016 Nov 1;273:175-190. doi: 10.1016/j.jneumeth.2016.09.004. Epub 2016 Sep 14.
Multiscale entropy (MSE) estimates the predictability of a signal over multiple temporal scales. It has been recently applied to study brain signal variability, notably during aging. The grounds of its application and interpretation remain unclear and subject to debate.
We used both simulated and experimental data to provide an intuitive explanation of MSE and to explore how it relates to the frequency content of the signal, depending on the amount of (non)linearity and stochasticity in the underlying dynamics.
The scaling and peak-structure of MSE curves relate to the scaling and peaks of the power spectrum in the presence of linear autocorrelations. MSE also captures nonlinear autocorrelations and their interactions with stochastic dynamical components. The previously reported crossing of young and old adults' MSE curves for EEG data appears to be mainly due to linear stochastic processes, and relates to young adults' EEG dynamics exhibiting a slower time constant.
We make the relationship between MSE curve and power spectrum as well as with a linear autocorrelation measure, namely multiscale root-mean-square-successive-difference, more explicit. MSE allows gaining insight into the time-structure of brain activity fluctuations. Its combined use with other metrics could prevent any misleading interpretations with regard to underlying stochastic processes.
Although not straightforward, when applied to brain signals, the features of MSE curves can be linked to their power content and provide information about both linear and nonlinear autocorrelations that are present therein.
多尺度熵（MSE）估计信号在多个时间尺度上的可预测性。最近它已被应用于研究脑信号变异性，尤其是在衰老过程中。其应用和解释的依据仍不明确且存在争议。
我们使用模拟数据和实验数据，对MSE进行直观解释，并探讨它如何与信号的频率成分相关，这取决于潜在动力学中的（非）线性和随机性程度。
在存在线性自相关的情况下，MSE曲线的标度和峰值结构与功率谱的标度和峰值相关。MSE还能捕捉非线性自相关及其与随机动力学成分的相互作用。先前报道的年轻人和老年人脑电图数据的MSE曲线交叉现象，似乎主要是由于线性随机过程，并且与年轻人脑电图动力学表现出较慢的时间常数有关。
我们使MSE曲线与功率谱以及与一种线性自相关测量方法（即多尺度均方根逐次差分）之间的关系更加明确。MSE有助于深入了解脑活动波动的时间结构。将其与其他指标结合使用，可以防止对潜在随机过程产生任何误导性解释。
尽管并不简单，但当应用于脑信号时，MSE曲线的特征可以与其功率内容相关联，并提供其中存在的线性和非线性自相关的信息。
