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Myoelectric signal analysis using neural networks.

M F Kelly¹, P A Parker, R N Scott

¹Dept. of Electr. Eng., New Brunswick Univ., Fredericton, NB.

IEEE Engineering in Medicine and Biology Magazine : the Quarterly Magazine of the Engineering in Medicine & Biology Society

|January 1, 1990

Summary

A discrete Hopfield network efficiently extracts time-series parameters from myoelectric signals (MES) faster than the SLS algorithm. Neural networks show promise for MES analysis and classifying muscular contractions.

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Area of Science:

Biomedical Engineering
Computational Neuroscience
Signal Processing

Background:

Myoelectric signal (MES) analysis is crucial for understanding muscle activity.
Existing algorithms like SLS have limitations in processing speed for MES data.
Neural networks offer potential for advanced signal analysis.

Purpose of the Study:

To evaluate the efficacy of a discrete Hopfield network for extracting time-series parameters from MES.
To compare the performance of the Hopfield network with the SLS algorithm.
To explore the use of neural networks for classifying different types of muscular contractions using MES.

Main Methods:

Utilized a discrete Hopfield network for functional minimization to extract MES time-series parameters.
Employed a two-layer perceptron trained with back-propagation.
Defined a two-dimensional signal space using signal parameters and power for classification.

Main Results:

The Hopfield network demonstrated a faster rate of parameter extraction from MES compared to the SLS algorithm.
The back-propagation trained perceptron successfully classified MES signals corresponding to different muscular contractions.
Neural networks proved effective in analyzing MES data.

Conclusions:

Discrete Hopfield networks offer a more efficient method for MES parameter extraction.
Neural networks, particularly perceptrons, are suitable for MES analysis and classification tasks.
Further research into neural network applications for myoelectric signal processing is recommended.