scholarly journals Design of Adaptive Filter Using Jordan/Elman Neural Network in a Typical EMG Signal Noise Removal

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
V. R. Mankar ◽  
A. A. Ghatol
Keyword(s):  
Neural Network ◽  
Low Frequency ◽  
Noise Removal ◽  
Regression Problem ◽  
Elman Neural Network ◽  
Signal Noise ◽  
Emg Signal ◽  
Multivariable Regression ◽  
Lower Magnitude ◽  
Range Of Values

The bioelectric potentials associated with muscle activity constitute the electromyogram (EMG). These EMG signals are low-frequency and lower-magnitude signals. In this paper, it is presented that Jordan/Elman neural network can be effectively used for EMG signal noise removal, which is a typical nonlinear multivariable regression problem, as compared with other types of neural networks. Different neural network (NN) models with varying parameters were considered for the design of adaptive neural-network-based filter which is a typical SISO system. The performance parameters, that is, MSE, correlation coefficient, N/P, and t, are found to be in the expected range of values.

scholarly journals Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
S. N. Kale ◽  
S. V. Dudul
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Biomedical Application ◽  
Noise Removal ◽  
Noise Signal ◽  
Modern Human ◽  
Emg Signal ◽  
Uniform Noise ◽  
Time Lagged ◽  
Hidden Layer

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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