Time-dependent physiological data sets are often difficult to interpret objectively. Biosignals such as electromyogram, electroencephalogram or single-neuron recordings can be interpreted using various linear and nonlinear methods. Each analysis technique aims at the explanation of different data features that might be visible or not to the naked eye. Here, we used linear decomposition based on machine learning to extract motor primitives (the time-dependent coefficients of muscle synergies) from the hindlimb electromyographic activity of mice during normal and mechanically perturbed locomotion. We set out to investigate the effects of calculation parameters and data quality on two nonlinear metrics derived from fractal analysis: The Higuchi's fractal dimension (HFD) and the Hurst exponent (H). Both HFD and H proved to be exceptionally sensitive to changes in motor primitives induced by external perturbations to locomotion. We discuss the potential pitfalls that might arise from fractal analysis by using examples based on surrogate data. We conclude giving some simple, data-driven suggestions to reduce the chance of misinterpretations when metrics such as HFD and H are applied to any biological signal containing elements of periodicity.

中文翻译：

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运动过程中肌肉活动模式的分形分析：陷阱以及如何避免它们。

与时间相关的生理数据集通常难以客观解释。可以使用各种线性和非线性方法来解释肌电图、脑电图或单神经元记录等生物信号。每种分析技术都旨在解释肉眼可见或不可见的不同数据特征。在这里，我们使用基于机器学习的线性分解从正常和机械扰动运动期间小鼠的后肢肌电活动中提取运动原语（肌肉协同的时间相关系数）。我们着手研究计算参数和数据质量对分形分析得出的两个非线性指标的影响：Higuchi 的分形维数 (HFD) 和 Hurst 指数 (H)。事实证明，HFD 和 H 都对运动的外部扰动引起的运动基元变化异常敏感。我们通过使用基于替代数据的示例来讨论分形分析可能出现的潜在缺陷。我们总结给出了一些简单的、数据驱动的建议，以减少当 HFD 和 H 等指标应用于任何包含周期性元素的生物信号时出现误解的机会。