scholarly journals EMG Signal Noise Removal Using Neural Netwoks

10.5772/23780 ◽  
2011 ◽  
Author(s):  
Vijay R.
Keyword(s):  
Noise Removal ◽  
Signal Noise ◽  
Emg Signal

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.

Noise Removal from EMG Signal Using Adaptive Enhanced Squirrel Search Algorithm

2020 ◽  
Vol 19 (04) ◽  
pp. 2050039
Author(s):  
B. Nagasirisha ◽  
V. V. K. D. V. Prasad
Keyword(s):  
Adaptive Filters ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Maximum Error ◽  
Noise Removal ◽  
Least Square ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Emg Signal

Electromyogram (EMG) signals are mostly affected by a large number of artifacts. Most commonly affecting artifacts are power line interference (PLW), baseline noise and ECG noise. This work focuses on a novel attenuation noise removal strategy which is concentrated on adaptive filtering concepts. In this paper, an enhanced squirrel search (ESS) algorithm is applied to remove noise using adaptive filters. The noise eliminating filters namely adaptive least mean square (LMS) filter and adaptive recursive least square (RLS) filters are designed, which is correlated with an ESS. This novel algorithm yields better performance than other existing algorithms. Here the performances are measured in terms of signal-to-noise ratio (SNR) in decibel, maximum error (ME), mean square error (MSE), standard deviation, simulation time and mean value difference. The proposed work has been implemented at the MATLAB simulation platform. Testing of their noise attenuation capability is also validated with different evolutionary algorithms namely squirrel search, particle swarm optimization (PSO), artificial bee colony (ABC), firefly, ant colony optimization (ACO) and cuckoo search (CS). The proposed work eliminates the noises and provides noise-free EMG signal at the output which is highly efficient when compared with existing methodologies. Our proposed work achieves 4%, 40%, 4%, 7%, 9% and 70% better performance than the literature mentioned in the results.

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.

Signal/Noise Ratio and Elemental Detectivity by Eels

1982 ◽  
Vol 40 ◽  
pp. 488-489
Author(s):  
R. F. Egerton
Keyword(s):  
Energy Loss ◽  
Electron Energy ◽  
Elemental Analysis ◽  
Electron Energy Loss Spectroscopy ◽  
Emission Spectroscopy ◽  
X Ray ◽  
Signal Noise ◽  
Characteristic Signal ◽  
Noise Ratio ◽  
Electron Energy Loss

An important parameter governing the sensitivity and accuracy of elemental analysis by electron energy-loss spectroscopy (EELS) or by X-ray emission spectroscopy is the signal/noise ratio of the characteristic signal.

Unified small-signal-noise model for active microwave devices

1993 ◽  
Vol 140 (1) ◽  
pp. 55 ◽  
Author(s):  
Z.R. Hu ◽  
Z.M. Yang ◽  
V.F. Fusco ◽  
J.A.C. Stewart
Keyword(s):  
Noise Model ◽  
Microwave Devices ◽  
Small Signal ◽  
Signal Noise
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