Multivariate approach for assessing electrode positioning in surface electromyography
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Date
2023-08
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University of New Brunswick
Abstract
Placing electrodes near or over an innervation zone has been shown to affect the quality and integrity of the recorded signals. The aim of this work was to investigate whether using pattern recognition to analyze electromyography data could lead to an automated approach for estimating the graded effect of an innervation zone on surface electromyography signal. Using a set of features from simulated electromyography signals as input, classification and regression algorithms were explored to predict the graded effect of an innervation zone. The regression technique was observed to be best suited for the application. The effects of physiological parameter variability between the training and test data sets were also investigated. Some physiological parameters, especially the innervation zone distribution and conduction velocity, were found to have the most impact on the performance of the regressor. Regression is a promising approach for subsequent research, especially with recorded data.