TY - JOUR
T1 - Analysis and classification of compressed EMG signals by wavelet transform via alternative neural networks algorithms
AU - Ozsert, M.
AU - Yavuz, O.
AU - Durak-Ata, L.
PY - 2011
Y1 - 2011
N2 - We propose intelligent methods for classifying three different muscle types, i.e. biceps, frontallis and abductor pollicis brevis muscles, with low computational complexity. For this aim, electromyogram (EMG) signals are recorded and modelled by using an auto-regressive (AR) model. As the size of the EMG signals is usually large, the computational complexity of artificial neural network (ANN) systems drastically increases. Therefore, in the proposed scheme EMG signals are pre-processed by using a wavelet transform and then they are modelled by employing an AR approach. The AR coefficients are used to train and test the ANNs. Experimental results show that the highest achieved classification accuracy is more than 95% in the case of EMG signals pre-processed by wavelet transform. The wavelet transform-based pre-processing significantly increases the performance rates compared to standard multilayer perceptron and general regression neural networks algorithms.
AB - We propose intelligent methods for classifying three different muscle types, i.e. biceps, frontallis and abductor pollicis brevis muscles, with low computational complexity. For this aim, electromyogram (EMG) signals are recorded and modelled by using an auto-regressive (AR) model. As the size of the EMG signals is usually large, the computational complexity of artificial neural network (ANN) systems drastically increases. Therefore, in the proposed scheme EMG signals are pre-processed by using a wavelet transform and then they are modelled by employing an AR approach. The AR coefficients are used to train and test the ANNs. Experimental results show that the highest achieved classification accuracy is more than 95% in the case of EMG signals pre-processed by wavelet transform. The wavelet transform-based pre-processing significantly increases the performance rates compared to standard multilayer perceptron and general regression neural networks algorithms.
KW - Artificial neural networks
KW - Auto-regresive model
KW - Cross-validation
KW - EMG
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=79958755059&partnerID=8YFLogxK
U2 - 10.1080/10255842.2010.485130
DO - 10.1080/10255842.2010.485130
M3 - Article
C2 - 20645198
AN - SCOPUS:79958755059
SN - 1025-5842
VL - 14
SP - 521
EP - 525
JO - Computer Methods in Biomechanics and Biomedical Engineering
JF - Computer Methods in Biomechanics and Biomedical Engineering
IS - 6
ER -