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A new and fast approach towards sEMG decomposition

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Abstract

The decomposition of high-density surface EMG (HD-sEMG) interference patterns into the contribution of motor units is still a challenging task. We introduce a new, fast solution to this problem. The method uses a data-driven approach for selecting a set of electrodes to enable discrimination of present motor unit action potentials (MUAPs). Then, using shapes detected on these channels, the hierarchical clustering algorithm as reported by Quian Quiroga et al. (Neural Comput 16:1661–1687, 2004) is extended for multichannel data in order to obtain the motor unit action potential (MUAP) signatures. After this first step, more motor unit firings are obtained using the extracted signatures by a novel demixing technique. In this demixing stage, we propose a time-efficient solution for the general convolutive system that models the motor unit firings on the HD-sEMG grid. We constrain this system by using the extracted signatures as prior knowledge and reconstruct the firing patterns in a computationally efficient way. The algorithm performance is successfully verified on simulated data containing up to 20 different MUAP signatures. Moreover, we tested the method on real low contraction recordings from the lateral vastus leg muscle by comparing the algorithm’s output to the results obtained by manual analysis of the data from two independent trained operators. The proposed method showed to perform about equally successful as the operators.

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References

  1. Abed-Meraim K, Wanzhi Q, Hua Y (1997) Blind system identification. P IEEE 85(8):1310–1322

    Article  Google Scholar 

  2. Blatt M, Wiseman S, Domany E (1996) Superparamagnetic clustering of data. Phys Rev Lett 76:3251–3254

    Article  PubMed  CAS  Google Scholar 

  3. Blok JH, van Dijk JP, Drost G, Zwarts MJ et al (2002) A high-density multichannel surface electromyography system for the characterization of single motor units. Rev Sci Instrum 73(4):1887–1898

    Article  CAS  Google Scholar 

  4. Castella M, Bianchi P, Chevreuil A et al (2006) A blind source separation framework for detecting CPM sources mixed by a convolutive MIMO filter. Signal Process 86(8):1950–1967

    Article  Google Scholar 

  5. Daube JR, Rubin DI (2009) Needle electromyography. Muscle Nerve 39(2):244–270

    Article  PubMed  Google Scholar 

  6. De Luca CJ, Hostage EC (2010) Relationship Between firing rate and recruitment threshold of motoneurons in voluntary isometric contractions. J Neurophysiol 104(2):1034–1046

    Article  PubMed  Google Scholar 

  7. De Luca CJ, Adam A, Wotiz R et al (2006) Decomposition of Surface EMG Signals. J Neurophysiol 96:1646–1657

    Article  PubMed  Google Scholar 

  8. Denny-Brown D (1949) Interpretation of the electromyogram. Arch Neurol Psychiatry 61:99–128

    Article  PubMed  CAS  Google Scholar 

  9. Donoho DL, Johnstone JM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455

    Article  Google Scholar 

  10. Dorfman LJ, Howard JE, McGill KC (1989) Motor unit firing rates and firing rate variability in the detection of neuromuscular disorders. Electroen Clin Neuro 73(2):215–224

    Article  CAS  Google Scholar 

  11. Drost G, Stegeman DF, Van Engelen BGM et al (2006) Clinical applications of high-density surface EMG: a systematic review. J Electromyogr Kinesiol 16:586–602

    Article  PubMed  Google Scholar 

  12. Florestal JR, Mathieu PA, Malanda A (2006) Automated decomposition of intramuscular electromyographic signals. IEEE T Bio-Med Eng 53(5):832–839

    Article  Google Scholar 

  13. Florestal JR, Mathieu PA, McGill KC (2009) Automatic decomposition of multichannel intramuscular EMG signals. J Electromyogr Kinesiol 19:1–9

    Article  PubMed  CAS  Google Scholar 

  14. Gazzoni M, Farina D, Merletti R (2004) A new method for the extraction and classification of single motor unit action potentials from surface EMG signals. J Neurosci Meth 136(2):165–177

    Article  Google Scholar 

  15. Gligorijević I, De Vos M, Blok JH et al (2011) Automated way to obtain motor units’ signatures and estimate their firing patterns during voluntary contractions using HD-sEMG. Conf Proc IEEE Eng Med Biol Soc 2011:4090–4093

    PubMed  Google Scholar 

  16. Gligorijević I, BTHM Sleutjes, M De Vos et al. (2012) Correcting electrode displacement errors in motor unit tracking using high density surface electromyography (HDsEMG). In: Proccessing of the 7th International Workshop on Biosignal Interpretation (BSI2012), Como, Italy

  17. Golub GH, Van Loan CF (1996) Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore

    Google Scholar 

  18. Holobar A, Zazula D (2007) Multichannel blind source separation using convolution kernel compensation. IEEE Trans Signal Process 55(9):4487–4496

    Article  Google Scholar 

  19. Holobar A, Minetto MA, Botter A et al (2010) Experimental analysis of accuracy in the identification of motor unit spike trains from high-density surface EMG. IEEE Trans Neural Syst Rehabil Eng 18(3):221–229

    Article  PubMed  Google Scholar 

  20. Kleine BU, van Dijk JP, Lapatki BG et al (2007) Using two-dimensional spatial information in decomposition of surface EMG signals. J Electromyogr Kinesiol 17:535–548

    Article  PubMed  Google Scholar 

  21. Kleine BU, van Dijk JP, Zwarts MJ et al (2008) Inter-operator agreement in decomposition of motor unit firings from high-density surface EMG. J Electromyogr Kinesiol 18:652–661

    Article  PubMed  Google Scholar 

  22. Lapatki BG, van Dijk JP, Jonas IE et al (2004) A thin, flexible multielectrode grid for high-density surface EMG. J Appl Physiol 96(1):1327–1336

    Google Scholar 

  23. Maathuis EM, Drenthen J, van Dijk JP et al (2008) Motor unit tracking with high-density surface EMG. J Electromyogr Kinesiol 18:920–930

    Article  PubMed  Google Scholar 

  24. McGill KC (2002) Optimal resolution of superimposed action potentials. IEEE T Bio-Med Eng 49(7):640–650

    Article  Google Scholar 

  25. Merletti R, Holobar A, Farina D (2008) Analysis of motor units with high-density surface electromyography. J Electromyogr Kinesiol 18:879–890

    Article  PubMed  Google Scholar 

  26. Nawab SH, Chang SS, De Luca CJ (2010) High-yield decomposition of surface EMG signals. J Clin Neurophysiol 121(10):1602–1615

    Article  Google Scholar 

  27. Quian Quiroga R, Nadasdy Z, Ben-Shaul Y (2004) Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput 16:1661–1687

    Article  PubMed  Google Scholar 

  28. Roeveld K, Stegeman DF, Falck B et al (1997) Motor unit size estimation: confrontation of surface EMG with macro EMG. J Electroen Clin Neuro 105:181–188

    Google Scholar 

  29. Xu Z, Xiao S, Chi Z (2001) ART2 Neural Network for Surface EMG Decomposition. Neural Comput Appl 10(1):29–38

    Article  Google Scholar 

  30. Zazula D, Holobar A (2005) An approach to surface EMG decomposition based on higher-order cumulants. Comput Meth Prog Bio 80(1):51–60

    Article  Google Scholar 

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Acknowledgments

We acknowledge financial support from: GOA MaNet, PFV/10/002 (OPTEC), FWO project G.0427.10 N (Integrated EEG-fMRI), IUAP P6/04 (DYSCO); IMEC SLT PhD Scholarship; M. De Vos obtained a Von Humboldt stipend.

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Authors and Affiliations

  1. Department of Electrical Engineering, SCD-SISTA, KU Leuven, Kasteelpark Arenberg 10, 3001, Leuven, Belgium

    Ivan Gligorijević, Bogdan Mijović, Sabine Van Huffel & Maarten De Vos

  2. IBBT Future Health Department, SCD-SISTA, KU Leuven, 3001, Leuven, Belgium

    Ivan Gligorijević, Bogdan Mijović, Sabine Van Huffel & Maarten De Vos

  3. Department of Neurology/Clinical Neurophysiology, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB, Nijmegen, The Netherlands

    Johannes P. van Dijk

  4. Department of Orthodontics, Center of Dentistry, Ulm University, Medical Centre, Ulm, Germany

    Johannes P. van Dijk

  5. Department of Clinical Neurophysiology, Erasmus MC Rotterdam, Rotterdam, The Netherlands

    Joleen H. Blok

  6. Department of Psychology, Neuroscience Lab, Oldenburg University, Oldenburg, Germany

    Maarten De Vos

Authors
  1. Ivan Gligorijević
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  2. Johannes P. van Dijk
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  3. Bogdan Mijović
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  4. Sabine Van Huffel
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  5. Joleen H. Blok
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  6. Maarten De Vos
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Correspondence to Ivan Gligorijević.

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Gligorijević, I., van Dijk, J.P., Mijović, B. et al. A new and fast approach towards sEMG decomposition. Med Biol Eng Comput 51, 593–605 (2013). https://doi.org/10.1007/s11517-012-1029-y

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  • DOI: https://doi.org/10.1007/s11517-012-1029-y

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