Decomposition of indwelling EMG signals

Journal of Applied Physiology

Volumn 105, Issue 2, 2008, Pages 700-710

  Nawab, S Hamid ^a,b   Wotiz, Robert P ^b   De Luca, Carlo J ^a,b  

^a BOSTON UNIVERSITY   (United States)

^b Boston University   (United States)

Author keywords

Detection of recruitment; Electromyographic signal; Force varying contractions; Signal decomposition; Superposition of action potentials

Indexed keywords

ACTION POTENTIAL; ARTICLE; ARTIFICIAL INTELLIGENCE; CONTROLLED STUDY; ELECTROMYOGRAM; HUMAN; MOTOR UNIT POTENTIAL; MUSCLE CONTRACTION; MUSCLE ISOMETRIC CONTRACTION; PRIORITY JOURNAL; ALGORITHM; ELECTROMYOGRAPHY; KNOWLEDGE BASE; NEUROPHYSIOLOGICAL RECRUITMENT; PHYSIOLOGY; REPRODUCIBILITY; SIGNAL PROCESSING; STATISTICAL ANALYSIS; STATISTICS;

ACTION POTENTIALS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; ELECTROMYOGRAPHY; HUMANS; KNOWLEDGE BASES; RECRUITMENT, NEUROPHYSIOLOGICAL; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 53449101057     PISSN: 87507587     EISSN: 15221601     Source Type: Journal    
DOI: 10.1152/japplphysiol.00170.2007     Document Type: Article

References (52)

1. Adam A, De Luca CJ. Recruitment order of motor units in human vastus lateralis muscle is maintained during fatiguing contractions. J Neurophysiol 90: 2919-2927, 2003.
2. Adam A, De Luca CJ. Firing rates of motor units in human vastus lateralis muscle during fatiguing isometric contractions. J Appl Physiol 99: 268-280, 2005.
3. Adam A, De Luca CJ, Erim Z. Hand dominance and motor unit firing behavior. J Neurophysiol 80: 1373-1382, 1998.
4. Andreassen S. Computerized analysis of motor unit firing. Prog Clin Neurophysiol 10: 150-163, 1983.
5. Black PE. Dictionary of Algorithms and Data Structures (Online). NIST. http://www.nist.gov/dads/HTML/greedyalgo.html [2005].
6. Broman H. Knowledge-based signal processing in the decomposition of myoelectric signals. IEEE Eng Med Biol Mag 7/2: 24-28, 1988.
7. Castañon DA. Efficient algorithms for finding the K best paths through a trellis. IEEE Trans Aerospace Electr Sys 26: 405-410, 1990.
8. Christodoulou CI, Pattichis CS. Unsupervised pattern recognition for the classification of EMG signals. IEEE Trans Biomed Eng 46: 169-178, 1999.
9. De Luca CJ, Forrest WJ. An electrode for recording single motor unit activity during strong muscle contractions. IEEE Trans Biomed Eng 19: 367-372, 1972.
10. De Luca CJ, LeFever RS, McCue MP, Xenakis AP. Behavior of human motor units in different muscles during linearly varying contractions. J Physiol 329: 113-128, 1982.
11. De Luca CJ, LeFever RS, McCue MP, Xenakis AP. Control scheme governing concurrently active human motor units during voluntary contractions. J Physiol 329: 129-142, 1982.
12. De Luca CJ, Adam A. Decomposition and analysis of intramuscular electromyographic signals. In: Modern Techniques in Neuroscience Research, edited by Windhorst U, Johansson H. Heidelberg: Springer, 1999, p. 757-776.
13. De Luca CJ, Adam A, Wotiz RL, Gilmore LD, Nawab SH. Decomposition of surface EMG signals. J Neurophysiol 96: 2769-2774, 2006.
14. Erim Z, Faisal Beg MF, Burke DT, De Luca CJ. Effects of aging on motor unit control properties. J Neurophysiol 82: 2081-2091, 1999.
15. Fang J, Agarwal GC, Shahani BT. Decomposition of EMG signal by wavelet spectrum matching. Proc 19th Ann Int Conf IEEE Eng Med Biol Soc, Chicago, IL, 1997, p. 1253-1256.
16. Florestal JR, Mathieu PA, Malanda A. Automated decomposition of intramuscular electromyographic signals. IEEE Trans Biomed Eng 53: 832-839, 2006.
17. Gerstein GL, Clark WA. Simultaneous studies of firing patterns in several neurons. Science 143: 1325-1327, 1964.
18. Glaser EM, Marks WB. The on-line separation of inter-leaved neuronal pulse sequences. Proc Rochester Conf Data Acquisition Biol Med, Rochester, NY, 1966, p. 137-156.
19. Guiheneuc P, Calamel J, Doncarli C, Gitton D, Michel C. Automatic detection and pattern recognition of single motor unit potentials in needle EMG. In: Computer-Aided Electromyography, edited by Desmedt JE. Basel: Karger, 1983, p. 73-127.
20. Hochstein L, Nawab SH, Wotiz R. An AI-based software architecture for a biomedical application. Proc 6th World Multiconf Systemics Cybernetics Informatics, Orlando, FL, 2002, p. 60-64.
21. Holobar A, Zazula D. Multichannel blind source separation using convolution kernel compensation. IEEE Trans Signal Processing 55: 4487-4496, 2007.
22. Kamen G, De Luca CJ. Unusual motor unit firing behavior in older adults. Brain Res 482: 136-140, 1989.
23. Keehn DG. An interactive spike separation technique. IEEE Trans Biomed Eng 13: 19-28, 1966.
24. Kleine BU, van Dijk JP, Lapatki BG, Zwatrs MJ, Stegeman DF. Using two-dimensional spatial information in decomposition of surface EMG signals. J Electromyogr Kinesiol 17: 535-548, 2007.
25. LeFever RS, De Luca CJ. Decomposition of action potential trains. Proc 8th Ann Meeting Soc Neurosci, St. Louis, MO, 1978, p. 229.
26. LeFever RS, De Luca CJ. A procedure for decomposing the myoelectric signal into its constituent action potentials. Part I: Technique, theory, and implementation. IEEE Trans Biomed Eng 29: 149-157, 1982.
27. LeFever RS, Xenakis AP, De Luca CJ. A procedure for decomposing the myoelectric signal into its constituent action potentials. Part II: Execution and test for accuracy. IEEE Trans Biomed Eng 29: 158-164, 1982.
28. Lesser VR, Nawab SH, Klassner FI. IPUS: an architecture for the integrated processing and understanding of signals. Artif Intell 77: 129-171, 1995.
29. Loudon GH, Jones NB, Sehmi AS. New signal processing techniques for the decomposition of EMG signals. Med Biol Eng Comput 30: 591-599, 1992.
30. Mambrito B, De Luca CJ. A technique for the detection, decomposition and analysis of the EMG signal. EEG Clin Neurophysiol 58: 175-188, 1984.
31. Masakado Y, Akaboshi K, Nagata M, Kimura A, Chino N. Motor unit firing behavior in slow and fast contractions of the first dorsal interosseous muscle of healthy men. EEG Clin Neurophysiol 976: 290-295, 1995.
32. Masuda T, De Luca CJ. A technique for detecting MUAP propagation from high threshold motor units. J Electromyogr Kinesiol 1: 75-80, 1991.
33. McGill KC, Cummins KL, Dorfman LJ. Automatic decomposition of the clinical electromyogram. IEEE Trans Biomed Eng 32: 470-477, 1985.
34. McGill KC, Lateva ZC, Marateb HR. EMGLAB: an interactive EMG decomposition program. J Neurosci Methods 149: 121-133, 2005.
35. Nawab SH, Lesser V. Integrated processing and understanding of signals. In: Symbolic and Knowledge Based Signal Processing, edited by Oppenheim AV, Nawab SH. Englewood Cliffs, NJ: Prentice-Hall, 1992, p. 251-285.
36. Nawab SH, Wotiz R, Hochstein L, De Luca CJ. Improved decomposition of intramuscular EMG signals. In: Proc 6th World Multiconf Systemics Cybernetics Informatics, Orlando, FL, 2002, p. 274-279.
37. Nawab SH, Wotiz R, Hochstein L, De Luca CJ. Next-generation decomposition of multi-channel EMG signals. Proc 2nd Joint Meeting IEEE Eng Med Biol Soc Biomed Eng Soc, Houston, TX, 2002, p. 36-37.
38. Nawab SH, Wotiz R, De Luca CJ. Resolving EMG pulse superpositions via utility maximization. Proc 8th World Multiconf Systemics Cybernetics Informatics, Orlando, FL, 2004, p. 233-236.
39. Nawab SH, Wotiz R, De Luca CJ. Improved resolution of pulse superpositions in a knowledge-based system for EMG decomposition. Proc 26th Int Conf IEEE Eng Med Biol Soc, San Francisco, CA, 2004, p. 69-71.
40. Nawab SH, Wotiz R, De Luca CJ. Multi-receiver precision decomposition of indwelling EMG signals. Proc 28th Int Conf IEEE Eng Med Biol Soc, New York, NY, 2006, p. 1252-1255.
41. Ren X, Hu X, Wang Z, Yan Z. MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition. Med Biol Eng Comput 44: 371-382, 2006.
42. Roark RM, Li JCL, Schaefer SD, Adam A, De Luca CJ. Multiple motor unit recordings of laryngeal muscles: the technique of vector laryngeal electromyography. Laryngoscope 112: 2196-2203, 2002.
43. Sauvage C, Manto M, Adam A, Roark R, Jissendi P, De Luca CJ. Ordered motor unit firing behavior in acute cerebellar stroke. J Neurophysiol 96: 2769-2774, 2006.
44. Simon W. The real-time sorting of neuro-electric action potentials in multiple unit studies. Electroencephalogr Clin Neurophysiol 18: 192-195, 1965.
45. Sogaard K. Motor unit recruitment pattern during low level static and dynamic contractions. Muscle Nerve 18: 292-300, 1995.
46. Stashuk DW, De Bruin H. Automatic decomposition of selective needledetected myoelectric signals. IEEE Trans Biomed Eng 35: 1-10, 1988.
47. Stashuk D, Qu Y. Robust method for estimating motor unit firing-pattern statistics. Med Biol Eng Comput 34: 50-57, 1996.
48. Von Neumann J, Morgenstern O. Theory of Games and Economic Behavior. Princeton, NJ: Princeton Univ., 1944.
49. Westad C, Westgaard RH. The influence of contraction amplitude and firing history on spike-triggered averaged trapezius motor unit potentials. J Physiol 562: 965-975, 2005.
50. Winograd JM, Nawab SH. A C++ software environment for the development of embedded signal processing systems. Proc IEEE Int Conf Acoustics Speech Signal Processing, Detroit, MI, 1995, p. 2715-2718.
51. Wolf JK, Viterbi AM, Dixon SG. Finding the best set of K paths through a trellis with applications to multitarget tracking. IEEE Trans Aerospace Electronic Syst 25: 287-296, 1989.
52. Zennaro D, Wellig P, Koch V, Moschytz G, Laubli T. A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients. IEEE Trans Biomed Eng 50: 58-69, 2003.