Motor unit characteristics in healthy subjects and those with postpoliomyelitis syndrome: A high‐density surface EMG study

Abstract

The purpose of this study was to identify optimal ways to detect neurogenic changes with high-density surface electromyography (HD-sEMG). For this purpose, we searched for the variables that most clearly discriminated between postpoliomyelitis and healthy subjects. We obtained HD-sEMG from the quadriceps muscle at different force levels in nine subjects with postpoliomyelitis syndrome and in matched healthy controls. Single motor unit action potentials (MUAPs), extracted from the HD-sEMG signal and the raw signal itself, were analyzed. Areas under the curve of the extracted MUAP waveform, indicating motor unit size, perfectly separated both groups. Raw signal analysis showed significant differences between groups for the monopolarly recorded amplitude up to 60% of maximal force and for the level of interference at higher force levels (40-100% force). We conclude that with HD-sEMG it is possible to detect neurogenic motor unit changes noninvasively, both by analysis of the raw signal itself and by analysis of extracted single MUAPs. The diagnostic yield of the single MUAP analysis is clearly higher. These findings point toward applications for clinical practice and invite further studies exploring the diagnostic value of HD-sEMG.

Inter-operator agreement in decomposition of motor unit firings from high-density surface EMG

“…The force level that yielded a decomposable surface EMG was only 1% MVC in most cases and below 5% MVC in all cases. This was lower than we had expected, based on earlier incomplete decomposition analysis (A1) of recordings from the quadriceps muscle (Drost et al, 2004). With this finding in mind we looked at some of the recordings again.…”

“…The onset was marked interactively in the monopolar MUAPs from the A3B3 dataset (Kleine et al, 2000). For each MUAP the maximum amplitude and area were taken as a measure of MU size (Drost et al, 2004;Roeleveld et al, 1998). Traditionally agreement between two measurements has been addressed in a scatter plot of both estimates, supplemented by correlation and regression analysis.…”

Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

“…The model for simulation of pathological changes in sEMG was introduced in [7]. The most investigated sEMG parameters include MFCV [33,38,39], MU size [9,32], frequency spectra [13,15], sEMG amplitude [9,22] and sEMG entropy [7,14]. However, although some studies have demonstrated feasibility of the detection of neuromuscular diseases from sEMG [9,14,30], the reported classification accuracy typically ranges from 70% to 80%.…”

“…Various sEMG techniques have been studied for the diagnosis of neuromuscular disorders, including conventional bipolar sEMG [13,22], linear electrode array [15] and matrix electrode array (high-density sEMG) [9,10,14,30,32]. The model for simulation of pathological changes in sEMG was introduced in [7].…”

Continuous Wavelet Analysis and Classification of Surface Electromyography Signals

“…The other common EMG uses needle electrodes penetrated into the muscle, thus signals obtained are focused on a particular muscle motor unit. Needle EMG is an invasive method which can cause stress to the patient involved [10], hence SEMG is a preferable method of gathering signals. Furthermore, SEMG obtains signals sourced from a group of muscle rather than one single muscle unit.…”