Roume Clément
IRIMAS UR UHA 7499, University of Haute-Alsace, Mulhouse, France.
Front Netw Physiol. 2023 Oct 31;3:1204757. doi: 10.3389/fnetp.2023.1204757. eCollection 2023.
The assessment of physiological complexity via the estimation of monofractal exponents or multifractal spectra of biological signals is a recent field of research that allows detection of relevant and original information for health, learning, or autonomy preservation. This tutorial aims at introducing Whittle's maximum likelihood estimator (MLE) that estimates the monofractal exponent of time series. After introducing Whittle's maximum likelihood estimator and presenting each of the steps leading to the construction of the algorithm, this tutorial discusses the performance of this estimator by comparing it to the widely used detrended fluctuation analysis (DFA). The objective of this tutorial is to propose to the reader an alternative monofractal estimation method, which has the advantage of being simple to implement, and whose high accuracy allows the analysis of shorter time series than those classically used with other monofractal analysis methods.
通过估计生物信号的单分形指数或多重分形谱来评估生理复杂性是一个新兴的研究领域，它能够检测出与健康、学习或自主维持相关的原始信息。本教程旨在介绍用于估计时间序列单分形指数的惠特尔最大似然估计器（MLE）。在介绍了惠特尔最大似然估计器并展示了构建该算法的每一步之后，本教程通过将其与广泛使用的去趋势波动分析（DFA）进行比较，讨论了该估计器的性能。本教程的目的是向读者提出一种替代的单分形估计方法，该方法具有易于实现的优点，并且其高精度允许分析比其他单分形分析方法传统使用的时间序列更短的时间序列。
