Myoelectric signal analysis using neural networks

Author

Kelly, M.F. ; Parker, P.A. ; Scott, R.N.

Author_Institution

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

Volume

9

Issue

1

fYear

1990

fDate

3/1/1990 12:00:00 AM

Firstpage

61

Lastpage

64

Abstract

It is shown that the capacity of a discrete Hopfield network for functional minimization allows it to extract the time-series parameters from a myoelectric signal (MES) at a faster rate than the previously used SLS algorithm. With a two-dimensional signal space consisting of one of the parameters and the signal power, a two-layer perceptron trained using back-propagation has been used to classify MES signals from different types of muscular contractions. The results suggest that neural networks may be suitable for MES analysis tasks and that further research in this direction is warranted.<>

Keywords

bioelectric potentials; muscle; neural nets; signal processing; discrete Hopfield network; functional minimization; muscular contractions; myoelectric signal analysis; neural networks; time-series parameters; two-dimensional signal space; two-layer perceptron; Artificial neural networks; Electrodes; Feature extraction; Muscles; Neural networks; Neural prosthesis; Pattern recognition; Prosthetics; Signal analysis;

fLanguage

English

Journal_Title

Engineering in Medicine and Biology Magazine, IEEE

Publisher

ieee

ISSN

0739-5175

Type

jour

DOI

10.1109/51.62909

Filename

62909