Linear mixed-effects models for the analysis of high-density electromyography with application to diabetic peripheral ne
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ORIGINAL ARTICLE
Linear mixed-effects models for the analysis of high-density electromyography with application to diabetic peripheral neuropathy Felipe Rettore Andreis 1,2 & Mateus Andre Favretto 1 & Sandra Cossul 1 & Luiz Ricardo Nakamura 3 & Pedro Alberto Barbetta 3 & Jefferson Luiz Brum Marques 1 Received: 3 September 2019 / Accepted: 26 April 2020 # International Federation for Medical and Biological Engineering 2020
Abstract 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. Keywords Linear mixed-effects models . High-density surface electromyography . Diabetic peripheral neuropathy
1 Introduction Diabetic peripheral neuropathy (DPN) is the most common type of neuropathic syndrome associated with diabetes mellitus. DPN results from a combination of various metabolic and vascular factors, and it is associated with several complications, such as distal weakness, premature fatigue, reduced muscle strength, foot ulcers and in severe cases lower limb * Felipe Rettore Andreis [email protected] 1
Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil
2
Department of Health Science and Technology, Center for Neuroplasticity and Pain (CNAP), SMI, Aalborg University, Aalborg, Denmark
3
Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil
amputation [1, 2]. DPN is a distal, length-dependent condition, affecting mainly muscles with a higher proportion of type I fibres, such as the tibialis anterior [3, 4]. The high-density surface electromyography (HD-sEMG) technique is a useful tool to prov
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