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

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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.

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Funding

The authors thank the Brazilian Government Funding Agencies Coordination for the Improvement of Higher Education Personnel (CAPES) and National Council for Scientific and Technological Development (CNPq) for MAF, SC and FRA postgrad scholarships and JLBM Research Productivity scholarship.

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Authors and Affiliations

  1. Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Felipe Rettore Andreis, Mateus Andre Favretto, Sandra Cossul & Jefferson Luiz Brum Marques

  2. Department of Health Science and Technology, Center for Neuroplasticity and Pain (CNAP), SMI, Aalborg University, Aalborg, Denmark

    Felipe Rettore Andreis

  3. Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil

    Luiz Ricardo Nakamura & Pedro Alberto Barbetta

Authors
  1. Felipe Rettore Andreis
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  2. Mateus Andre Favretto
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  3. Sandra Cossul
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  4. Luiz Ricardo Nakamura
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  5. Pedro Alberto Barbetta
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  6. Jefferson Luiz Brum Marques
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Correspondence to Felipe Rettore Andreis.

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Ethical approval

All procedures of this study followed the principles of the Declaration of Helsinki and were approved by the Human Research Ethics Committee of the Federal University of Santa Catarina (Protocol Number: 2.390.994). Written informed consent was obtained from all participants before the experiment.

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Andreis, F.R., Favretto, M.A., Cossul, S. et al. Linear mixed-effects models for the analysis of high-density electromyography with application to diabetic peripheral neuropathy. Med Biol Eng Comput 58, 1625–1636 (2020). https://doi.org/10.1007/s11517-020-02181-1

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  • DOI: https://doi.org/10.1007/s11517-020-02181-1

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