Resolving Superimposed MUAPs Using Particle Swarm Optimization

This paper presents an algorithm to resolve superimposed action potentials encountered during the decomposition of electromyographic signals. The algorithm uses particle swarm optimization with a variety of features including randomization, crossover, and multiple swarms. In a simulation study involving realistic superpositions of two to five motor-unit action potentials, the algorithm had an accuracy of 98%.

[1]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[2]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[3]  Thiemo Krink,et al.  The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers , 2002, PPSN.

[4]  Dezhong Yao,et al.  A feasibility study of EEG dipole source localization using particle swarm optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[5]  J. Salerno,et al.  Using the particle swarm optimization technique to train a recurrent neural model , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[6]  Michael N. Vrahatis,et al.  Modification of the Particle Swarm Optimizer for Locating All the Global Minima , 2001 .

[7]  Terence Soule,et al.  Breeding swarms: a GA/PSO hybrid , 2005, GECCO '05.

[8]  D. Stashuk,et al.  Resolving superimposed motor unit action potentials , 2007, Medical and Biological Engineering and Computing.

[9]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[10]  Ronald S. Lefever,et al.  A Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials - Part I: Technique, Theory, and Implementation , 1982, IEEE Transactions on Biomedical Engineering.

[11]  Paolo Bonato,et al.  Decomposition of superimposed waveforms using the cross time frequency transform , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[12]  Réjean Plamondon,et al.  A Genetic Algorithm for the Resolution of Superimposed Motor Unit Action Potentials , 2007, IEEE Transactions on Biomedical Engineering.

[13]  Bennett L. Fox,et al.  Algorithm 647: Implementation and Relative Efficiency of Quasirandom Sequence Generators , 1986, TOMS.

[14]  Rui J. P. de Figueiredo,et al.  Separation of superimposed signals by a cross-correlation method , 1982, ICASSP.

[15]  R. Wotiz,et al.  Improved resolution of pulse superpositions in a knowledge-based system EMG decomposition , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Mahamed G. H. Omran.  Particle swarm optimization methods for pattern recognition and image processing , 2006 .

[17]  D Stashuk,et al.  EMG signal decomposition: how can it be accomplished and used? , 2001, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[18]  Kevin C. McGill,et al.  RESOLVING SUPERIMPOSED MUAPS BY PARTICLE SWARM OPTIMIZATION , 2008 .

[19]  Kevin C. McGill,et al.  Optimal resolution of superimposed action potentials , 2002, IEEE Transactions on Biomedical Engineering.