Optimization of neural network for efficient EMG signal classification

Author

Ahsan, Md Rezwanul ; Ibrahimy, M.I. ; Khalifa, Othman Omran

Author_Institution

Dept. of Electr. & Comput. Eng., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia

fYear

2012

fDate

10-12 April 2012

Firstpage

1

Lastpage

6

Abstract

This paper illustrates the classification of Electromyography (EMG) signals through designing and optimization of artificial neural network. The EMG signals obtained for different kinds of hand movements, which are processed to extract the features. Extracted time and time frequency based feature sets are used to train the neural network. A back-propagation neural network with Levenberg-Marquardt training algorithm has been utilized for the classification. The results show that the designed network is optimized for 10 hidden neurons and able to efficiently classify single channel EMG signals with an average rate of 88.4%.

Keywords

backpropagation; electromyography; feature extraction; medical signal processing; neural nets; optimisation; signal classification; Levenberg-Marquardt training algorithm; artificial neural network optimization; backpropagation neural network; electromyography signal classification; feature extraction; single channel EMG signal classification; time-frequency based feature sets; Artificial neural networks; Biological neural networks; Classification algorithms; Electromyography; Feature extraction; Neurons; Training; Back-Propagation; EMG Signal Classification; Electromyography; Levenberg-Marquardt Algorithm; Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and its Applications (ISMA), 2012 8th International Symposium on

Conference_Location

Sharjah

Print_ISBN

978-1-4673-0860-1

Type

conf

DOI

10.1109/ISMA.2012.6215165

Filename

6215165