Decomposition of surface EMG signals into single fiber action potentials by means of neural networks

Author

Graupe, O. ; Vern, B. ; Gruener, G. ; Field, A. ; Huang, Q.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Illinois Univ., Chicago, IL, USA

fYear

1989

Firstpage

1008

Abstract

A neural network approach to decomposing surface EMG signals into a multitude of single muscle-fiber action potentials (SFAP) is described. This decomposition yields the signal forms of the SFAPs and allows the particular SFAPs to be localized relative to the recording (surface) electrodes. The goal is to allow the physician and the medical researcher to observe the waveforms and localize SFAPs below the surface of the skin in a noninvasive manner and without discomforting the patient. The approach utilizes a Hopfield neural network

Keywords

bioelectric potentials; computerised signal processing; neural nets; Hopfield neural network; decomposing surface EMG signals; multitude of single muscle-fiber action potentials; neural networks; noninvasive measurement; single fiber action potentials; surface EMG signals; surface electrodes; waveform recording; Biomedical electrodes; Biomedical imaging; Electromyography; Hopfield neural networks; Image reconstruction; Medical diagnostic imaging; Neural networks; Probability distribution; Skin; Surface reconstruction;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1989., IEEE International Symposium on

Conference_Location

Portland, OR

Type

conf

DOI

10.1109/ISCAS.1989.100522

Filename

100522