Automatic identification of motor unit action potential trains from electromyographic signals using fuzzy techniques

Abstract

A technique is proposed that allows automatic decomposition of electromyographic (EMG) signals into their constituent motor unit action potential trains (MUAPTs). A specific iterative algorithm with a classification method using fuzzy-logic techniques was developed. The proposed classification method takes into account imprecise information, such as waveform instability and irregular firing patterns, that is often encountered in EMG signals. Classification features were determined by the combining of time position and waveform information. Statistical analysis of inter-pulse intervals and spike amplitude provided an accurate estimation of features used in the classification step. Algorithm performance was evaluated using simulated EMG signals composed of up to six different discharging motor units corrupted with white noise. The algorithm was then applied to real signals recorded by a high spatial resolution surface EMG device based on a Laplacian spatial filter. On six groups of 20 simulated signals, the decomposition algorithm performed with a maximum and an average mean error rate of 2.13% and 1.37%, respectively. On real surface EMG signals recorded at different force levels (from 10% to 40% of the maximum voluntary contraction), the algorithm correctly identified 21 MUAPTs, compared with the 29 MUAPTs identified by an experienced neurophysiologist. The efficiency of the decomposition on surface EMG signals makes this method very attractive for non-invasive investigation of physiological muscle properties. However, it can also be used to decompose intramuscularly recorded EMG signals.

Surface EMG signal alterations in Carpal Tunnel syndrome: a pilot study

“…Since in contractions at 80% MVC the interference and the degree of superposition among MUAPs is too high to allow a correct signal decomposition (Stashuk 2001;Chauvet et al 2003), the procedure was applied for 10, 20 and 30% MVC contractions only.…”

EMG Signal Decomposition Using Motor Unit Potential Train Validity

“…The IPUS framework assists the algorithm by decreasing the search space using template energy and inter-discharge interval (IDI) information extracted from the N MUPTs. A number of EMG signal decomposition methods that use fuzzy logic-based classifiers have been developed [68], [77], [78], [88], [128], [128]. Chauvet et al [128] developed a fuzzy classifier to assign…”

Performance of three electromyogram decomposition algorithms as a function of signal to noise ratio: Assessment with experimental and simulated data

“…In most decomposition schemes, an automated algorithm detects and clusters each MUAP firing, typically with expert manual editing performed thereafter. Signal processing methods for automated decomposition were pioneered by DeLuca and colleagues [7], [8]; with numerous variations and alternative approaches proposed and studied thereafter [2], [4], [9]- [17].…”