Linear mixed-effects models for the analysis of high-density electromyography with application to diabetic peripheral neuropathy.

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.