TY - GEN
T1 - Decomposition of electromyographic signal by principal component analysjs of wavelet coefficjents
AU - Yamada, Rie
AU - Ushiba, Junichi
AU - Tomita, Yutaka
AU - Masakado, Yoshihisa
PY - 2003/1/1
Y1 - 2003/1/1
N2 - Electromyographic (EMG) signals are the superposition of activities of multiple motor units (MUS). Therefore it is necessary to decompose the EMG signal in order to reveal the mechanisms pertaining to muscle and nerw control. Various techniques hare been devised with regards to EMG decomposition. A recently proposed method using wavelet analysis required manual selection of appropriate wavelet coefficients for action potential (AP) clustering. However the accuracy of this method depends heavily on the operators' ability to select suitable wavelet coefficients. To avoid this subjective ambiguilty we are proposing a new method ahich employs the principal component analysis on all wavelet coefficients to identify the distinguishable features of APs. The present method can decompose EMG automatically and unambiguously, from data input to clustering. Furthermore, our experimental results have shown that the decomposition accuracy was slightly higher than that of the conventional wavelet method.
AB - Electromyographic (EMG) signals are the superposition of activities of multiple motor units (MUS). Therefore it is necessary to decompose the EMG signal in order to reveal the mechanisms pertaining to muscle and nerw control. Various techniques hare been devised with regards to EMG decomposition. A recently proposed method using wavelet analysis required manual selection of appropriate wavelet coefficients for action potential (AP) clustering. However the accuracy of this method depends heavily on the operators' ability to select suitable wavelet coefficients. To avoid this subjective ambiguilty we are proposing a new method ahich employs the principal component analysis on all wavelet coefficients to identify the distinguishable features of APs. The present method can decompose EMG automatically and unambiguously, from data input to clustering. Furthermore, our experimental results have shown that the decomposition accuracy was slightly higher than that of the conventional wavelet method.
KW - Decomposition
KW - Electromyographic (EMG) signal
KW - Principal component analysis
KW - Wavelet coefficients
UR - http://www.scopus.com/inward/record.url?scp=33645233019&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33645233019&partnerID=8YFLogxK
U2 - 10.1109/APBME.2003.1302612
DO - 10.1109/APBME.2003.1302612
M3 - Conference contribution
AN - SCOPUS:33645233019
T3 - APBME 2003 - IEEE EMBS Asian-Pacific Conference on Biomedical Engineering 2003
SP - 118
EP - 119
BT - APBME 2003 - IEEE EMBS Asian-Pacific Conference on Biomedical Engineering 2003
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE EMBS Asian-Pacific Conference on Biomedical Engineering 2003, APBME 2003
Y2 - 20 October 2003 through 22 October 2003
ER -