Motor unit innervation zone localization based on robust linear regression analysis

IEEE Trans. Neural Syst. Rehabil. Eng.

Chen²,

Zhang

et al. 2023

This study presents a novel method to estimate a muscle's innervation zone (IZ) location from monopolar high density surface electromyography (EMG) signals. Based on the fact that 2 nd principal component coefficients derived from principal component analysis (PCA) are linearly related with the time delay of different channels, the channels located near the IZ should have the shortest time delays. Accordingly, we applied a novel method to estimate a muscle's IZ based on PCA. The performance of the developed method was evaluated by both simulation and experimental approaches. The method based on 2 nd principal component of monopolar high density surface EMG achieved a comparable performance to cross-correlation analysis of bipolar signals when noise was simulated to be independently distributed across all channels. However, in simulated conditions of specific channel contamination, the PCA based method achieved superior performance than the cross-correlation method. Experimental high density surface EMG was recorded from the biceps brachii of 9 healthy subjects during maximum voluntary contractions. The PCA and cross-correlation based methods achieved high agreement, with a difference in IZ location of 0.47 ± 0.4 IED (inter-electrode distance = 8 mm). The results indicate that analysis of 2 nd principal component coefficients provides a useful approach for IZ estimation using monopolar high density surface EMG.

Section: Introductionmentioning

confidence: 99%

A Novel Muscle Innervation Zone Estimation Method Using Monopolar High Density Surface Electromyography

IEEE Trans. Neural Syst. Rehabil. Eng.

Chen²,

Zhang

et al. 2023

“…To address these issues, a more delicate or realistic model is required in a future study. In addition, only conventional IZ estimation methods based on visual inspection, amplitude measurement and cross-correlation were used in this study, while more advanced methods for IZ estimation were not tested [21][22][23][24][25][26][27][28][29].…”

Section: Discussionmentioning

confidence: 99%

“…In fact, various methods have been developed to improve muscle IZ detection from electrode array surface EMG, using signal processing techniques including Radon transform, optical flow, linear regression, three-dimensional imaging, etc. [21][22][23][24][25][26][27][28][29]. Most of studies use interference surface EMG from voluntary contractions to perform IZ estimation.…”

Section: Introductionmentioning

confidence: 99%

Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study

Chen

et al. 2021

Biosensors

Self Cite

Surface electromyography (EMG) recorded by a linear or 2-dimensional electrode array can be used to estimate the location of muscle innervation zones (IZ). There are various neurophysiological factors that may influence surface EMG and thus potentially compromise muscle IZ estimation. The objective of this study was to evaluate how surface-EMG-based IZ estimation might be affected by different factors, including varying degrees of motor unit (MU) synchronization in the case of single or double IZs. The study was performed by implementing a model simulating surface EMG activity. Three different MU synchronization conditions were simulated, namely no synchronization, medium level synchronization, and complete synchronization analog to M wave. Surface EMG signals recorded by a 2-dimensional electrode array were simulated from a muscle with single and double IZs, respectively. For each situation, the IZ was estimated from surface EMG and compared with the one used in the model for performance evaluation. For the muscle with only one IZ, the estimated IZ location from surface EMG was consistent with the one used in the model for all the three MU synchronization conditions. For the muscle with double IZs, at least one IZ was appropriately estimated from interference surface EMG when there was no MU synchronization. However, the estimated IZ was different from either of the two IZ locations used in the model for the other two MU synchronization conditions. For muscles with a single IZ, MU synchronization has little effect on IZ estimation from electrode array surface EMG. However, caution is required for multiple IZ muscles since MU synchronization might lead to false IZ estimation.

“…The IZ can be identified through EMG signals recorded by a linear electrode array or a matrix of electrodes placed over the muscle ( Drost et al, 2006 ; Barbero et al, 2012 ; Piccoli et al, 2014 ; Campanini et al, 2022 ). Most investigators have estimated the location of the IZ based on surface EMG recordings of voluntary muscle contractions and processing the signals in a single differential or bipolar configuration ( Ostlund et al, 2007 ; Mesin et al, 2009 ; Enck et al, 2010 ; Barbero et al, 2011 ; Beck et al, 2012 ; Ullah et al, 2014 ; Marateb et al, 2016 ; Liu et al, 2019 ; Mancebo et al, 2019 ; Liu et al, 2020 ; Zhang et al, 2020 ), whereas few have processed monopolar signals for IZ estimation ( Rodriguez-Falces, 2017 ). When EMG signals are processed in a differential configuration the IZ location may correspond to either a reversal in EMG signal polarity between two adjacent channels along the muscle fibers, or the smallest amplitude in a single channel.…”

Section: Introductionmentioning

confidence: 99%

Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform

Lu²,

Chen³

et al. 2023

Front. Physiol.

Self Cite

This study examined methods for estimating the innervation zone (IZ) of a muscle using recorded monopolar high density M waves. Two IZ estimation methods based on principal component analysis (PCA) and Radon transform (RT) were examined. Experimental M waves, acquired from the biceps brachii muscles of nine healthy subjects were used as testing data sets. The performance of the two methods was evaluated by comparing their IZ estimations with manual IZ detection by experienced human operators. Compared with manual detection, the agreement rate of the estimated IZs was 83% and 63% for PCA and RT based methods, respectively, both using monopolar high density M waves. In contrast, the agreement rate was 56% for cross correlation analysis using bipolar high density M waves. The mean difference in estimated IZ location between manual detection and the tested method was 0.12 ± 0.28 inter-electrode-distance (IED) for PCA, 0.33 ± 0.41 IED for RT and 0.39 ± 0.74 IED for cross correlation-based methods. The results indicate that the PCA based method was able to automatically detect muscle IZs from monopolar M waves. Thus, PCA provides an alternative approach to estimate IZ location of voluntary or electrically-evoked muscle contractions, and may have particular value for IZ detection in patients with impaired voluntary muscle activation.