LAUSR

Electromyographic (EMG) signals are utilized to analyze the neuromuscular disorders. Machine learning algorithms have been employed as a decision support system to detect neuromuscular disorders. EMG signals contain noise from different sources, such as electrical and electronic instruments and movement artifacts. In this paper, the multiscale principal component analysis (MSPCA) has been used to remove the impulsive noise from the EMG signals. Then, the dual-tree complex wavelet transform (DT-CWT) is utilized for feature extraction, and the rotation forest ensemble classifier is employed for the recognition of EMG signals. In addition, the performance of several classifiers with rotation forest has been studied. An efficient combination of DT-CWT and rotation forest achieved good performance, using tenfold cross validation regarding the total classification accuracy. Results are promising and showed that the rotation forest achieved an accuracy of 99.7% with clinical EMG signals using support vector machine and 96.6% with simulated EMG signals using the artificial neural network (ANN).