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Kale, S. N.; Dudul, S. V. (2009)
Publisher: Hindawi Publishing Corporation
Journal: Applied Computational Intelligence and Soft Computing
Languages: English
Types: Article
Subjects: Electronic computers. Computer science, QA75.5-76.95, Article Subject
Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN) can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors developed an optimal FTLRNN model, which removes the noise from the EMG signal. Results show that the proposed optimal FTLRNN model has an MSE (Mean Square Error) as low as 0.000067 and 0.000048, correlation coefficient as high as 0.99950 and 0.99939 for noise signal and EMG signal, respectively, when validated on the test dataset. It is also noticed that the output of the estimated FTLRNN model closely follows the real one. This network is indeed robust as EMG signal tolerates the noise variance from 0.1 to 0.4 for uniform noise and 0.30 for Gaussian noise. It is clear that the training of the network is independent of specific partitioning of dataset. It is seen that the performance of the proposed FTLRNN model clearly outperforms the best Multilayer perceptron (MLP) and Radial Basis Function NN (RBF) models. The simple NN model such as the FTLRNN with single-hidden layer can be employed to remove noise from EMG signal.
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    • Reaz, M. B. I., Hussain, M. S., Mohd-Yasin, F.. Techniques of EMG signal analysis: detection, processing, classification and applications. Biological Procedures Online. 2006; 8 (1): 11-35
    • Wu, S.-I., Zheng, H.. Stock index forecasting using recurrent neural networks.
    • de Veaux, R. D., Schumi, J., Schweinsberg, J., Ungar, L. H.. Prediction intervals for neural networks via nonlinear regression. Technometrics. 1998; 40 (4): 273-282
    • Barman, R., Prasad Kumar, B., Pandey, P. C., Dube, S. K.. Tsunami travel time prediction using neural networks. Geophysical Research Letters. 2006; 33 (16)-6
    • Sadat-Hashemi, S. M., Kazemnejad, A., Lucas, C., Badie, K.. Predicting the type of pregnancy using artificial neural networks and multinomial logistic regression: a comparison study. Neural Computing & Applications. 2005; 14 (3): 198-202
    • Fredric, H. M., Kostanic, I.. Principles of Neurocomputing for Science & Engineering. 2000
    • Boggs, P. T., Byrd, R. H., Schnabel, R. B.. A stable and efficient algorithm for nonlinear orthogonal distance regression. SIAM Journal on Scientific Computing. 1987; 8 (6): 1052-1078
    • Lewis, P. A., Stevens, J. G.. Nonlinear modeling of time series using Multivariate Adaptive Regression Splines (MARS). 1990 (ADA222710)
    • Haykin, S.. Neural Networks: A Comprehensive Foundation. 1999
    • Principe, J. C., Euliano, N., Lefebvre, W. C.. Neural & Adaptive Systems: Fundamentals through Simulations. 2000
    • Demuth, H., Beale, M.. Neural Network Toolbox: User's Guide, Version 3.0. 1998
    • Cybenko, G.. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems. 1989; 2 (4): 303-314
    • Qin, S.-Z., Su, H.-T., McAvoy, T. J.. Comparison of four neural net learning methods for dynamic system identification. IEEE Transactions on Neural Networks. 1992; 3 (1): 122-130
    • Powell, M. J. D.. Radial basis functions for multivariable interpolation: a review. : 143-167
    • Light, W., Cheney, E. W., Chui, C. K., Schumaker, L. L.. Ridge functions, sigmoidal functions and neural networks. Approximation Theory VII. 1992: 163-206
    • Cover, T. M.. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers. 1965; 14 (3): 326-334
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