Myoelectric signal segmentation and classification using wavelets based neural networks

Author

Al-Assaf, Yousef ; Al-Nashash, Hasan

Author_Institution

Sch. of Eng., American Univ. of Sharjah, United Arab Emirates

Volume

2

fYear

2001

fDate

2001

Firstpage

1820

Abstract

In this paper a method for Myoelectric signal (MES) segmentation and classification is proposed. The classical moving average technique augmented with Principal Components Analysis (PCA), and time-frequency analysis were used for segmentation. Multiresolution Wavelet Analysis (MRWA) was adopted as an effective feature extraction technique while Artificial Neural Networks (ANN) was used for MES classification. Results of classifying four elbow and wrist movements gave 94.9% sensitivity and 94.9% positive predictivity.

Keywords

backpropagation; electromyography; feature extraction; feedforward neural nets; medical signal processing; moving average processes; neuromuscular stimulation; principal component analysis; signal classification; time-frequency analysis; wavelet transforms; backpropagation training; biceps branchii; effective feature extraction; elbow flexion-extension; localized neuromuscular activity; moving average technique; multilayer feedforward networks; multiresolution wavelet analysis; myoelectric signal; principal components analysis; prosthesis control; signal classification; signal segmentation; time-frequency analysis; triceps branchii; wavelets based neural networks; wrist pronation-supination; Artificial neural networks; Elbow; Feature extraction; Frequency; Neural networks; Principal component analysis; Prosthetics; Wavelet analysis; Wavelet coefficients; Wrist;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE

ISSN

1094-687X

Print_ISBN

0-7803-7211-5

Type

conf

DOI

10.1109/IEMBS.2001.1020575

Filename

1020575