An EMG classification method based on wavelet transform [and ANN]

Author

Cai, Liyu ; Wang, Zhizhong ; Zhang, Haihong

Author_Institution

Dept. of Biomed. Eng., Shanghai Jiaotong Univ., China

Volume

1

fYear

1999

fDate

1999

Abstract

This paper presents the application of an artificial neural network technique together with a feature extraction method, viz., wavelet transform, for the classification of EMG signals. The architecture of ANN used in the classification is a three-layer feedforward network which implements the backpropagation of error learning algorithm. After training, the network with wavelet coefficients was able to classify four forearm motions with an average accuracy of 90%. The wavelet transform thus provides a potentially powerful technique for real time preprocessing of EMG signals prior to classification

Keywords

backpropagation; electromyography; feature extraction; feedforward neural nets; medical signal processing; signal classification; wavelet transforms; EMG signals; Mallat algorithm; artificial neural network technique; backpropagation; error learning algorithm; feature extraction method; forearm motions; real time preprocessing; signal classification method; surface electrode signals; three-layer feedforward network; wavelet transform; Artificial neural networks; Backpropagation algorithms; Electromyography; Feature extraction; Pattern classification; Pattern recognition; Signal resolution; Wavelet analysis; Wavelet coefficients; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint

Conference_Location

Atlanta, GA

ISSN

1094-687X

Print_ISBN

0-7803-5674-8

Type

conf

DOI

10.1109/IEMBS.1999.802643

Filename

802643