This article demonstrates the power and flexibility of linear mixed-effects models (LMEMs) to investigate high-density surface electromyography (HD-sEMG) signals. The potentiality of the model is illustrated by investigating the root mean squared value of HD-sEMG signals in the tibialis anterior muscle of healthy (n = 11) and individuals with diabetic peripheral neuropathy (n = 12). We started by presenting the limitations of traditional approaches by building a linear model with only fixed effects. Then, we showed how the model adequacy could be increased by including random effects, as well as by adding alternative correlation structures. The models were compared with the Akaike information criterion and the Bayesian information criterion, as well as the likelihood ratio test. The results showed that the inclusion of the random effects of intercept and slope, along with an autoregressive moving average correlation structure, is the one that best describes the data (p < 0.01). Furthermore, we demonstrate how the inclusion of additional variance structures can accommodate heterogeneity in the residual analysis and therefore increase model adequacy (p < 0.01). Thus, in conclusion, we suggest that adopting LMEM to repeated measures such as electromyography can provide additional information from the data (e.g. test for alternative correlation structures of the RMS value), and hence provide new insights into HD-sEMG-related work.

中文翻译：

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用于高密度肌电图分析的线性混合效应模型，应用于糖尿病周围神经病变。

本文演示了线性混合效应模型（LMEMs）用于研究高密度表面肌电图（HD-sEMG）信号的功能和灵活性。该模型的潜力通过研究健康（n = 11）和患有糖尿病性周围神经病变（n = 12）的胫骨前肌中HD-sEMG信号的均方根值来说明。我们首先通过建立仅具有固定效果的线性模型来介绍传统方法的局限性。然后，我们展示了如何通过包括随机效应以及添加其他相关结构来增加模型的适当性。将模型与Akaike信息准则和Bayes信息准则以及似然比检验进行比较。结果表明，包含截距和斜率的随机效应以及自回归移动平均相关结构是最能描述数据的方法（p <0.01）。此外，我们证明了包含其他方差结构如何能够在残差分析中适应异质性，从而增加模型的适用性（p <0.01）。因此，总而言之，我们建议采用LMEM进行重复测量（例如肌电图）可以从数据中提供其他信息（例如，测试RMS值的替代相关结构），从而为HD-sEMG相关工作提供新见解。我们证明了包含其他方差结构如何能够在残差分析中适应异质性，从而增加模型的适用性（p <0.01）。因此，总而言之，我们建议采用LMEM进行重复测量（例如肌电图）可以从数据中提供其他信息（例如，测试RMS值的替代相关结构），从而为HD-sEMG相关工作提供新的见解。我们证明了包含其他方差结构如何能够在残差分析中适应异质性，从而增加模型的适用性（p <0.01）。因此，总而言之，我们建议采用LMEM进行重复测量（例如肌电图）可以从数据中提供其他信息（例如，测试RMS值的替代相关结构），从而为HD-sEMG相关工作提供新的见解。