Generative topographic mapping applied to clustering and visualization of motor unit action potentials

BioSystems

Volumn 82, Issue 3, 2005, Pages 273-284

  Andrade, Adriano O ^a   Nasuto, Slawomir ^a   Kyberd, Peter ^b   Sweeney Reed, Catherine M ^a  

^a UNIVERSITY OF READING   (United Kingdom)

^b UNIVERSITY OF NEW BRUNSWICK   (Canada)

Author keywords

Cluster analysis; Electromyography; Generative topographic mapping; Motor unit action potentials; Self organizing map

Indexed keywords

CLUSTER ANALYSIS;

ALGORITHM; ARTICLE; CLUSTER ANALYSIS; DATA ANALYSIS; ELECTROMYOGRAPHY; ELECTROPHYSIOLOGY; INTERMETHOD COMPARISON; MOLECULAR MODEL; MOTOR UNIT POTENTIAL; NERVE CELL NETWORK; NEUROMUSCULAR FUNCTION; PRINCIPAL COMPONENT ANALYSIS;

EID: 27844544682     PISSN: 03032647     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystems.2005.09.004     Document Type: Article

References (27)

1. J.V. Basmajian, and C.J.D. Luca Muscles Alive: Their Functions Revealed by Electromyography 5th ed. 1985 Williams and Wilkins USA
2. A. Ben-Hur, A. Elisseeff, and I. Guyon A stability based method for discovering structure in clustered data Proceedings of the Pacific Symposium on Biocomputing 2002 6 17
3. C.M. Bishop, and M. Svensen Developments of the generative topographic mapping Neurocomputing 21 1998 203 224
4. C.M. Bishop, M. Svensen, and C.K.I. Willians The generative topographic mapping Neural Comput. 10 1 1998 215 234
5. J.M. Buhmann Data clustering and learning M.A. Arbib The Handbook of Brain and Neural Networks 1st ed. 2001 MIT Press 308 312
6. C.I. Christodoulou, and C.S. Pattichis Unsupervised pattern recognition for the classification of EMG signals IEEE Trans. Biomed. Eng. 46 2 1999 169 199
7. B. Efron, and R.J. Tibshirani An Introduction to the Bootstrap of Monographs on Statistics and Probability, vol. 57 1st ed. 1993 Chapman & Hall USA
8. H. Etawil, and D. Stashuk Resolving superimposed motor unit action potentials Med. Biol. Eng. Comput. 34 1 1996 33 40
9. M. Halkidi, Y. Batistakis, and M. Vazirgiannis Cluster validity methods: Part I SIGMOD Rec. 31 3 2002 19 27
10. M. Halkidi, Y. Batistakis, and M. Vazirgiannis Clustering validity checking methods: Part II SIGMOD Rec. 31 2 2002 40 45
11. M.H. Hassoun, C. Wang, and A.R. Spitzer NNERVE: neural network extraction of repetitive vectors for electromyography. Part I. Algorithm IEEE Trans. Biomed. Eng. 41 11 1994 1039 1051
12. A.K. Jain, and R.C. Dubes Algorithms for Clustering Data 1st ed. 1988 Prentice-Hall New Jersey
13. R.L. Joynt, R.F. Erlandson, S.J. Wu, and C.M. Wang Electromyography interference pattern decomposition Arch. Phys. Med. Rehabil. 72 8 1991 567 572
14. Law, M.H., Jain, A.K., 2003. Cluster validity by bootstrapping partitions. Technical Report MSU-CSE-03-5. Department of Computer Science and Engineering, Michigan State University.
15. R.S. LeFever, and C.J. De Luca A procedure for decomposing the myoelectric signal into its constituent action potentials. Part I. Technique, theory, and implementation IEEE Trans. Biomed. Eng. 29 3 1982 149 157
16. B. Mambrito, and C.J. De Luca A technique for the detection, decomposition and analysis of the EMG signal Electroencephalogr. Clin. Neurophysiol. 58 1984 175 188
17. T.M. Martinetz, S.G. Berkovich, and K.J. Schulten Neural-gas network for vector quantization and its application to time-series prediction IEEE Trans. Neural Networks 4 4 1993 558 569
18. K.C. McGill, K.L. Cummins, and L.J. Dorfman Automatic decomposition of the clinical electromyogram IEEE Trans. Biomed. Eng. BME-32 7 1985 470 477
19. I.T. Nabney NetLab: Algorithms for Pattern Recognition, Advances in Pattern Recognition 3rd ed. 2004 Springer UK
20. D.N. Politis Computer-intensive methods in statistical analysis IEEE Signal Process. Mag. 15 1 1998 39 55
21. H. Ritter Self-organizing feature maps M.A. Arbib The Handbook of Brain and Neural Networks 1st ed. 2001 MIT Press 1005 1010
22. D. Stashuk EMG Signal Decomposition: how can it be accomplished and used? J. Electromyogr. Kinesiol. 11 2001 151 173
23. D. Stashuk, and G.M. Paoli Robust supervised classification of motor unit action potentials Med. Biol. Eng. Comput. 36 1998 75 82
24. D.W. Stashuk, and R.K. Naphan Probabilistic inference-based classification applied to myoelectric signal decomposition IEEE Trans. Biomed. Eng. 39 4 1992 346 355
25. Svensen, J.F.M., 1998. GTM: the generative topographic mapping. Ph.D. Thesis. Aston University.
26. D. Zennaro, P. Wellig, V.M. Koch, G.S. Moschytz, and T. Laubli A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients IEEE Trans. Biomed. Eng. 50 1 2003 58 69
27. A.M. Zoubir, and B. Boashah The bootstrap and its application in signal processing IEEE Signal Process. Mag. 15 1 1998 55 76