Classification of EMG signals using PCA and FFT

Journal of Medical Systems

Volumn 29, Issue 3, 2005, Pages 241-250

  Güler, Nihal Fatma ^a   Koçer, Sabri ^a  

^a GAZI UNIVERSITY   (Turkey)

Author keywords

Backpropagation (BP); Fast Fourier transform (FFT); Multilayer perceptron (MLP); Principal components analysis (PCA); Support vector machine (SVM)

Indexed keywords

ADOLESCENT; ADULT; ARTICLE; CALCULATION; CHILD; CLINICAL ARTICLE; CONTROLLED STUDY; ELECTROMYOGRAPHY; FOURIER TRANSFORMATION; HUMAN; INFANT; MYOPATHY; NEUROMUSCULAR DISEASE; NEUROPATHY; PERFORMANCE; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL ANALYSIS; ULNAR NERVE;

ADOLESCENT; ADULT; CHILD; CHILD, PRESCHOOL; ELECTROMYOGRAPHY; FEMALE; FOURIER ANALYSIS; HUMANS; INFANT; INFANT, NEWBORN; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); NEUROMUSCULAR DISEASES; PRINCIPAL COMPONENT ANALYSIS; ROC CURVE; ULNAR NERVE;

EID: 21544468321     PISSN: 01485598     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10916-005-5184-7     Document Type: Article

References (22)

1. Kumaravel, N., and Kavitha, V., Automatic diagnosis of neuromuscular disease using neural network. Biomed. Sci. Instrum. 90:245-250, 1994.
2. Butchal, F., An Introduction to EMG, Copenhagen, Gyldendal, 1957.
3. Frize, M., Ennett, M., Stevenson, M., and Trigg, C. E., Clinical decision support for intensive care unit using ANN. Med. Eng. Phys. 23:217-222, 52001.
4. Savelberg, H. H., and Herzog, W., Prediction of dynamic tendon forces from electromyographic signals: An artificial neural network approach. J. Neurosci. Methods 30:65-74, 1997.
5. Pattichis, C. S., Schofield, I. S., and Fawcett, P., A morphological analysis of the macro motor unit potential. Med. Eng. Phys. 21(10):673-679, 1999.
6. Nussbaum, M. A., Chaffin, D. B., and Martin, B. J., A back-propagation neural network model of lumbar muscle recruitment during moderate static exertions. J. Biomech. 28(9):1015-1024, 1995.
7. Bigland-Ritchie, B., Donovan, E. F., and Roussos, C. S., Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal efforts. J. Appl. Physiol. 51(5):1300-1305, 1981.
8. Blinowska, A., Verroust, J., and Cannet, G., The determination of motor unit characteristics from the low frequency electromyographic power spectra. Electromyogr. Clin. Neurophysiol. 19:281-290, 1979.
9. Hiraiwa, A., Shimohara, K., and Tokimaga, Y., EMG pattern analysis and classification by neural network and application to prosthetic forearm control. IEEE Int. Joint Conf. Neural Netw., Singapore 1214-1219, 1991.
10. Farry, K. A., and Walker, I. D., Myoelectric teleoperation of a complex robotic hand. IEEE Trans. Robotics Aut. 12(5):775-788, 1996.
11. Sarbadhikari, S. N., A neural network confirms that physical exercise reverses EEG changes in depressed rats. Med. Eng. Phys. 17(18):579-582, 1995.
12. Perez, M. A., and , Nussbaum, M. A., Principal components analysis as an evaluation and classification tool for lower torso sEMG data. J. Biomech. 36:1225-1229, 2003.
13. Pfeiffer, G., and Kunze, K., Discriminant classification of motor unit potentials (MUPs) successfully separates neurogenic and myopathic conditions. A comparison of multi- and univariate diagnostical algorithms for MUP analysis. Electroenceph. Clin. Neurophysiol. 97:191-207, 1995.
14. Vapnik, V. N., Statistical Learning Theory, Wiley Series on Adaptive and Learning Systems for Signal Processing, Communications and Control, Wiley, New York, 1998.
15. Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., and Scholkopf, B., Support vector machines. IEEE Intell. Syst. 13(4):18-28, 1998.
16. Millet-Roig, J., Ventura-Galiano, R., Chorro-Gasco, F. J., and Cebrian, A., Support vector machine for arrhythmia discrimination with wavelet transform-based feature selection. IEEE Comput. Cardiol. 407-410, 2000.
17. Bishop, C. M., Neural Networks for Pattern Recognition, Oxford University Press, 1995.
18. Fredric, M. H., and Ivica, K., Principles of Neurocomputing for Science and Engineering, McGraw-Hill, New York, 2001.
19. Cortes, C., and Vapnik, V., Support Vector Machines, Machine Learning, 1995.
20. Pricipe, J. C., Neil, R. E., and Lefebvre, W. C., Neural and Adaptive Systems Fundementals Through Simulations, John Wiley & Sons, 2000.
21. Hanley, J. A., and McNeil, B. J., The meaning and use of the area under the Receiver Operating Characteristic (ROC) curve. Radiology 143:29-36, 1982.
22. Fuglsang-Frederiksen, A., and Ronager, J., EMG power spectrum, turns-amplitude analysis and motor unit potential duration in neuromuscular disorders. J. Neurol. Sci. 97(1) 81-91, 1990.