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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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References

  1. Tesfaye S (2011) Recent advances in the management of diabetic distal symmetrical polyneuropathy. J Diabetes Investig 2:33–42. https://doi.org/10.1111/j.2040-1124.2010.00083.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Parasoglou P, Rao S, Slade JM (2017) Declining skeletal muscle function in diabetic peripheral neuropathy. Clin Ther 39:1–19. https://doi.org/10.1016/j.clinthera.2017.05.001

    Article  Google Scholar 

  3. Butugan MK, Sartor CD, Watari R, Martins MCS, Ortega NRS, Vigneron VAM, Sacco ICN (2014) Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol 24:465–472. https://doi.org/10.1016/j.jelekin.2014.04.007

    Article  PubMed  Google Scholar 

  4. Oberbach A, Bossenz Y, Lehmann S, Niebauer J, Adams V, Paschke R, Schon MR, Bluher M, Punkt K (2006) Altered Fiber distribution and fiber-specific glycolytic and oxidative enzyme activity in skeletal muscle of patients with type 2 diabetes. Diabetes Care 29:895–900. https://doi.org/10.2337/diacare.29.04.06.dc05-1854

    Article  CAS  PubMed  Google Scholar 

  5. Merletti R, Aventaggiato M, Botter A, Holobar A, Marateb H, Vieira TMM (2010) Advances in surface EMG: recent progress in detection and processing techniques. Crit Rev Biomed Eng 38:305–345. https://doi.org/10.1615/CritRevBiomedEng.v38.i4.10

    Article  PubMed  Google Scholar 

  6. Suda EY, Gomes AA, Butugan MK, Sacco ICN (2016) Muscle fiber conduction velocity in different gait phases of early and late-stage diabetic neuropathy. J Electromyogr Kinesiol 30:263–271. https://doi.org/10.1016/j.jelekin.2016.08.006

    Article  PubMed  Google Scholar 

  7. Watanabe K, Gazzoni M, Holobar A, Miyamoto T, Fukuda K, Merletti R, Moritani T (2013) Motor unit firing pattern of vastus lateralis muscle in type 2 diabetes mellitus patients. Muscle Nerve 48:806–813. https://doi.org/10.1002/mus.23828

    Article  PubMed  Google Scholar 

  8. Suda EY, Madeleine P, Hirata RP, Samani A, Kawamura TT, Sacco ICN (2017) Reduced complexity of force and muscle activity during low level isometric contractions of the ankle in diabetic individuals. Clin Biomech 42:38–46. https://doi.org/10.1016/j.clinbiomech.2017.01.001

    Article  CAS  Google Scholar 

  9. Merletti R, Knaflitz M, De Luca CJ (1990) Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. J Appl Physiol 69:1810–1820. https://doi.org/10.1152/jappl.1990.69.5.1810

    Article  CAS  PubMed  Google Scholar 

  10. Almeida S, Riddell MC, Cafarelli E (2008) Slower conduction velocity and motor unit discharge frequency are associated with muscle fatigue during isometric exercise in type 1 diabetes mellitus. Muscle Nerve 37:231–240. https://doi.org/10.1002/mus.20919

    Article  CAS  PubMed  Google Scholar 

  11. Merletti R, Farina D (2016) Surface electromyography: physiology, engineering, and applications, 1st edn. IEEE Press Series on Biomedical Engineering, Hoboken

    Book  Google Scholar 

  12. Matthews JN, Altman DG, Campbell MJ, Royston P (1990) Analysis of serial measurements in medical research. BMJ 300:230–235. https://doi.org/10.1136/bmj.300.6719.230

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Littell RC, Pendergast J, Natarajan R (2000) Modelling covariance structure in the analysis of repeated measures data. Stat Med 19:1793–1819. https://doi.org/10.1002/1097-0258(20000715)19:13<1793::AID-SIM482>3.0.CO;2-Q

    Article  CAS  PubMed  Google Scholar 

  14. Lazic SE (2010) The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? BMC Neurosci 11:5. https://doi.org/10.1186/1471-2202-11-5

    Article  PubMed  PubMed Central  Google Scholar 

  15. Galbraith S, Daniel JA, Vissel B (2010) A study of clustered data and approaches to its analysis. J Neurosci 30:10601–10608. https://doi.org/10.1523/JNEUROSCI.0362-10.2010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Aarts E, Dolan CV, Verhage M, Van der Sluis S (2015) Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives. BMC Neurosci 16:1–15. https://doi.org/10.1186/s12868-015-0228-5

    Article  Google Scholar 

  17. Schober P, Vetter TR (2018) Repeated measures designs and analysis of longitudinal data: if at first you do not succeed-try, try again. Anesth Analg 127:569–575. https://doi.org/10.1213/ANE.0000000000003511

    Article  PubMed  PubMed Central  Google Scholar 

  18. Quené H, Van Den Bergh H (2004) On multi-level modeling of data from repeated measures designs: a tutorial. Speech Commun 43:103–121. https://doi.org/10.1016/j.specom.2004.02.004

    Article  Google Scholar 

  19. Gueorguieva R, Krystal JH (2004) Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry. Arch Gen Psychiatry 61:310–317. https://doi.org/10.1001/archpsyc.61.3.310

    Article  PubMed  Google Scholar 

  20. Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38:963–974

    Article  CAS  PubMed  Google Scholar 

  21. Demidenko E (2013) Mixed models: theory and applications with R. John Wiley & Sons

  22. Pinheiro J, Bates D (2006) Mixed-effects models in S and S-PLUS. Springer Science & Business Media

    Google Scholar 

  23. Matuschek H, Kliegl R, Vasishth S, Baayen H, Bates D (2017) Balancing type I error and power in linear mixed models. J Mem Lang 94:305–315. https://doi.org/10.1016/j.jml.2017.01.001

    Article  Google Scholar 

  24. Cheng J, Edwards LJ, Maldonado-Molina MM, Komro KA, Muller KE (2009) Real longitudinal data analysis for real people: building a good enough mixed model. Stat Med 29:504–520. https://doi.org/10.1002/sim.3775

    Article  Google Scholar 

  25. West BT, Galecki AT (2011) An overview of current software procedures for fitting linear mixed models. Am Stat 65:274–282. https://doi.org/10.1198/tas.2011.11077

    Article  PubMed Central  Google Scholar 

  26. De Livera AM, Zaloumis SO, Simpson JUA (2014) Models for the analysis of repeated continuous outcome measures in clinical trials. Respirology 19:155–161. https://doi.org/10.1111/resp.12217

    Article  PubMed  Google Scholar 

  27. Lininger M, Spybrook J, Cheatham CC (2015) Hierarchical linear model: thinking outside the traditional repeated-measures analysis-of-variance box. J Athl Train 50:438–441. https://doi.org/10.4085/1062-6050-49.5.09

    Article  PubMed  PubMed Central  Google Scholar 

  28. Murphy D, Pituch K (2009) The performance of multilevel growth curve models under an autoregressive moving average process. J Exp Educ 77:255–282. https://doi.org/10.3200/JEXE.77.3.255-284

    Article  Google Scholar 

  29. Favretto MA, Cossul S, Andreis FR, Balotin AF, Marques JLB (2018) High density surface EMG system based on ADS1298-front end. IEEE Lat Am Trans 16:1616–1622. https://doi.org/10.1109/TLA.2018.8444157

    Article  Google Scholar 

  30. Favretto MA, Cossul S, Andreis FR, Marques JLB (2019) Evaluation of rate of muscular force development in type 2 diabetic individuals with and without diabetic peripheral neuropathy. In: XXVI Brazilian Congress on Biomedical Engineering. pp 31–36

  31. Diggle P, Diggle PJ, Heagerty P et al (2002) Analysis of longitudinal data. Oxford University Press

  32. Verbeke G, Molenberghs G (2009) Linear mixed models for longitudinal data. Springer Science & Business Media

    Google Scholar 

  33. West BT, Welch KB, Galecki AT (2014) Linear mixed models: a practical guide using statistical software. Chapman and Hall/CRC

  34. Akaike H (1998) Information theory and an extension of the maximum likelihood principle: selected papers of Hirotugu Akaike. Springer, New York, NY

  35. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464

    Article  Google Scholar 

  36. Pinheiro J, Bates D, DegRoy S, et al (2018) nlme: linear and nonlinear mixed effects models

  37. R Core Team (2018) R: a language and environment for statistical computing

  38. Kwok OM, West SG, Green SB (2007) The impact of misspecifying the within-subject covariance structure in multiwave longitudinal multilevel models: a Monte Carlo study. Multivar Behav Res 42:557–592. https://doi.org/10.1080/00273170701540537

    Article  Google Scholar 

  39. Allen MD, Kimpinski K, Doherty TJ, Rice CL (2015) Decreased muscle endurance associated with diabetic neuropathy may be attributed partially to neuromuscular transmission failure. J Appl Physiol 118:1014–1022. https://doi.org/10.1152/japplphysiol.00441.2014

    Article  PubMed  PubMed Central  Google Scholar 

  40. González-Izal M, Malanda A, Gorostiaga E, Izquierdo M (2012) Electromyographic models to assess muscle fatigue. J Electromyogr Kinesiol 22:501–512. https://doi.org/10.1016/j.jelekin.2012.02.019

    Article  PubMed  Google Scholar 

  41. Muthén BO, Curran PJ (1997) General longitudinal modeling of individual differences in experimental designs: a latent variable framework for analysis and power estimation. Psychol Methods 2:371–402. https://doi.org/10.1037/1082-989X.2.4.371

    Article  Google Scholar 

  42. Andreis FR, Favretto MA, Cossul S, Barbetta PA, Marques JLB (2019) Reliability of maximal voluntary isometric contraction of ankle dorsiflexion in male subjects. In: Costa-Felix R, Machado J, Alvarenga A (eds) XXVI Brazilian congress on biomedical engineering. Springer, Singapore, pp 353–357

    Chapter  Google Scholar 

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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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