Improving myoelectric signal classification using wavelet packets and principal components analysis

Author

Englehart, K. ; Hudgins, B. ; Park, P.A. ; Stevenson, M.

Author_Institution

Dept. of Electr. & Comput. Eng., New Brunswick Univ., Fredericton, NB, Canada

Volume

1

fYear

1999

fDate

1999

Abstract

An accurate and computationally efficient means of classifying surface myoelectric signals has been the subject of considerable research effort in recent years. This work demonstrates how this may be achieved, using a wavelet packet based feature set in conjunction with principal components analysis

Keywords

electromyography; medical signal processing; principal component analysis; signal classification; time-frequency analysis; wavelet transforms; EMG; computationally efficient; dimensionality reduction; feature set; linear discriminant analysis; multilayer perceptron; myoelectric signal classification; pattern recognition; principal components analysis; short-time FT; time-frequency method; transient bursts; wavelet packets; Biomedical computing; Fourier transforms; Linear discriminant analysis; Pattern classification; Principal component analysis; Signal analysis; Steady-state; Testing; Wavelet analysis; Wavelet packets;

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.1999.802647

Filename

802647