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The Scientific World Journal
Volume 2012 (2012), Article ID 478952, 5 pages
Research Article

Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra

Department of Electrical and Electronics Engineering, Faculty of Architecture and Engineering, Batman University, 72060 Batman, Turkey

Received 2 September 2012; Accepted 12 October 2012

Academic Editors: T. Arendt and J. Benito-Leon

Copyright © 2012 Necmettin Sezgin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The analysis and classification of electromyography (EMG) signals are very important in order to detect some symptoms of diseases, prosthetic arm/leg control, and so on. In this study, an EMG signal was analyzed using bispectrum, which belongs to a family of higher-order spectra. An EMG signal is the electrical potential difference of muscle cells. The EMG signals used in the present study are aggressive or normal actions. The EMG dataset was obtained from the machine learning repository. First, the aggressive and normal EMG activities were analyzed using bispectrum and the quadratic phase coupling of each EMG episode was determined. Next, the features of the analyzed EMG signals were fed into learning machines to separate the aggressive and normal actions. The best classification result was 99.75%, which is sufficient to significantly classify the aggressive and normal actions.