Signal analysis of electromyogram by artificial neural network

Author

Lo, T.F. ; Chan, F.H.Y. ; Lam, F.K. ; Poon, P.W.F.

Author_Institution

Dept. of Electr. & Electron. Eng., Hong Kong Univ., Hong Kong

fYear

1994

fDate

13-16 Apr 1994

Firstpage

535

Abstract

During strong contraction, electromyogram (EMG) becomes a noise-like “interference pattern” composed of trains of motor-unit action potential (MUAP). With its adaptive properties, an artificial neural network (ANN) system is proposed and applied to the analysis of EMG for MUAP´s detection. Features of MUAPs are extracted and fed into the ANN system for on-line training in which the number of classes is not fixed. Then the ANN recognises the signal based on the properties of the training samples. The performance of the system has been tested with different configurations of the ANN and different parameters of computer-simulated EMG signals. The system gives a recognition rate of about 80% for one MUAP with a firing rate of 5 Hz. The recognition rate decreases to 70% or less if the firing rate or the number of different MUAPs increases

Keywords

bioelectric potentials; medical signal processing; muscle; 5 Hz; artificial neural network; computer-simulated EMG signals; electromyogram signal analysis; firing rate; motor-unit action potential trains; noise-like interference pattern; training sample properties; Artificial neural networks; Electromyography; Filtering; Filters; Frequency; Neural networks; Noise reduction; Signal analysis; Signal processing; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on

Print_ISBN

0-7803-1865-X

Type

conf

DOI

10.1109/SIPNN.1994.344856

Filename

344856