NNERVE: Neural Network Extraction of Repetitive Vectors for Electromyography—Part II: Performance Analysis

IEEE Transactions on Biomedical Engineering

Volumn 41, Issue 11, 1994, Pages 1053-1061

  Hassoun, Mohamad H ^a   Wang, Chuanming ^a   Spitzer, A Robert ^b  

^a WAYNE STATE UNIVERSITY   (United States)

^b WILLIAM BEAUMONT HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ARTICLE; DECOMPOSITION; ELECTROMYOGRAPHY; MOTOR PERFORMANCE; MOTOR UNIT; NERVE CELL NETWORK; NONHUMAN; SIGNAL NOISE RATIO; SIGNAL PROCESSING;

ALGORITHMS; COMPARATIVE STUDY; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROMYOGRAPHY; EVALUATION STUDIES; HUMAN; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); NEUROMUSCULAR DISEASES; PREDICTIVE VALUE OF TESTS; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 0028079951     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/10.335843     Document Type: Article

References (14)

1. J. A. Desmedt, Ed., Computer-Aided Electromyography: Progress in Clinical Neurophysiology, vol. 10. Basel, Switzerland: Karger, 1983.
2. L. J. Dorfman and K. C. McGill, “AAEE minimonograph #29: Automatic quantitative electromyography,” Muscle & Nerve, vol. 11, pp. 804-818, 1988.
3. R. S. LeFever and C. J. De Luca, “A procedure for decomposing the myoelectric signal into its constituent action potentials: I. Technique, theory and implementation,” IEEE Trans. Biomed. Eng., vol. BME-29, pp. 149-157, 1982.
4. R. S. LeFever, A. P. Xenakis and C. J. De Luca, “A procedure for decomposing the myoelectric signal into its constituent action potentials: 11. Execution and test for accuracy,” IEEE Trans. Biomed. Eng., vol. BME-29, pp. 158-164, 1982.
5. K. C. McGill, “A method for quantitating the clinical electromyogram,” Ph.D. dissertation, Stanford University, Stanford, CA, 1984.
6. E. C. McGill, K. L. Cummins and L. J. Dorfman, “Automatic decomposition of the clinical electromyogram,” IEEE Trans. Biomed. Eng., vol. BME-32, pp. 470-477, 1985.
7. M. H. Hassoun, C. Wang and A. R. Spitzer, “NNERVE: Neural network extraction of repetitive vectors for electromyography—Part I: Algorithm,” vol. 41, no. 11, pp. 1039-1052, Nov. 1994.
8. A. R. Spitzer, S. Shen and M. Hassoun, “Identification of unstable motor unit potential waveforms for stability analysis,” Muscle & Nerve, vol 12, p. 757, 1989.
9. M. H. Hassoun, C. Wang and A. R. Spitzer, “Electromyogram decomposition via unsupervised dynamic multilayer neural network,” in Proc. Int. Joint Conf, on Neural Networks, Baltimore, MD, June 1992.
10. K. C. McGill and L. J. Dorfman, “High-resolution alignment of sampled waveforms,” IEEE Trans. Biomed. Eng., vol. BME-31, pp. 462-468, 1984.
11. A. R. Webb and D. Lowe, “The optimal internal representation of multilayer classifier networks performs nonlinear discriminant analysis,” Neural Networks, vol. 3, pp. 367-375, 1990.
12. F. Kanaya and S. Miyake, “Bayes statistical behavior and valid generalization of pattern classifying neural networks,” IEEE Trans. Neural Networks, vol. 2, no. 4, pp. 471-475, 1991.
13. C. J. DeLuca, “Physiology and mathematics of myoelectrical signals,” IEEE Trans. Biomed. Eng., vol. BME-26, pp. 313-325, 1979.
14. C. Wang, “A robust system for automated decomposition of the electromyogram utilizing a neural network architecture,” Ph.D. dissertation, Wayne State Univ., Detroit, MI, 1991.