Single sensor detection and classification of multiple sources by higher-order spectra

1993 ◽  
Vol 140 (6) ◽  
pp. 350 ◽  
Author(s):  
M.C. Doǧan ◽  
J.M. Mendel
2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Necmettin Sezgin

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.


2006 ◽  
Author(s):  
Jeongmok Cho ◽  
Taegeun Jung ◽  
Dong-Hyeon Kim ◽  
Nam Huh ◽  
Tae-Whee Joung ◽  
...  

2009 ◽  
Vol 33 (1) ◽  
pp. 42-50 ◽  
Author(s):  
K. C. Chua ◽  
V. Chandran ◽  
U. Rajendra Acharya ◽  
C. M. Lim

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