Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra
Author
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-10-24
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Natural & Life Sciences (Multidisciplinary)
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Sezgin, Necmettin. 2012. Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra. The Scientific World Journal،Vol. 2012, no. 2012, pp.1-5.
https://search.emarefa.net/detail/BIM-474761
Modern Language Association (MLA)
Sezgin, Necmettin. Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra. The Scientific World Journal No. 2012 (2012), pp.1-5.
https://search.emarefa.net/detail/BIM-474761
American Medical Association (AMA)
Sezgin, Necmettin. Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra. The Scientific World Journal. 2012. Vol. 2012, no. 2012, pp.1-5.
https://search.emarefa.net/detail/BIM-474761
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-474761