scholarly journals Two-Source Validation of Progressive FastICA Peel-Off for Automatic Surface EMG Decomposition in Human First Dorsal Interosseous Muscle

2018 ◽  
Vol 28 (09) ◽  
pp. 1850019 ◽  
Author(s):  
Maoqi Chen ◽  
Xu Zhang ◽  
Zhiyuan Lu ◽  
Xiaoyan Li ◽  
Ping Zhou
Keyword(s):  
Motor Unit ◽  
Electrode Array ◽  
Surface Emg ◽  
High Density ◽  
Motor Unit Action Potential ◽  
First Dorsal Interosseous ◽  
First Dorsal Interosseous Muscle ◽  
Density Surface ◽  
Motor Unit Action ◽  
Semg Signals

This study aims to assess the accuracy of a novel high density surface electromyogram (SEMG) decomposition method, namely automatic progressive FastICA peel-off (APFP), for automatic decomposition of experimental electrode array SEMG signals. A two-source method was performed by simultaneous concentric needle EMG and electrode array SEMG recordings from the human first dorsal interosseous (FDI) muscle, using a protocol commonly applied in clinical EMG examination. The electrode array SEMG was automatically decomposed by the APFP while the motor unit action potential (MUAP) trains were also independently identified from the concentric needle EMG. The degree of agreement of the common motor unit (MU) discharge timings decomposed from the two different categories of EMG signals was assessed. A total of 861 and 217 MUs were identified from the 114 trials of simultaneous high density SEMG and concentric needle EMG recordings, respectively. Among them 168 common (MUs) were found with a high average matching rate of [Formula: see text] for the discharge timings. The outcomes of this study show that the APFP can reliably decompose at least a subset of MUs in the high density SEMG signals recorded from the human FDI muscle during low contraction levels using a protocol analog to clinical EMG examination.

scholarly journals Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters

2020 ◽  
Vol 10 (15) ◽  
pp. 5099 ◽  
Author(s):  
Khalil Ullah ◽  
Khalil Khan ◽  
Muhammad Amin ◽  
Muhammad Attique ◽  
Tae-Sun Chung ◽  
...  
Keyword(s):  
Action Potential ◽  
Motor Unit ◽  
Linear Structure ◽  
Surface Emg ◽  
Motor Unit Action Potential ◽  
Multi Scale ◽  
Motor Unit Action ◽  
Spatio Temporal ◽  
Unit Action ◽  
Semg Signals

Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.

scholarly journals Error reduction in EMG signal decomposition

2014 ◽  
Vol 112 (11) ◽  
pp. 2718-2728 ◽  
Author(s):  
Joshua C. Kline ◽  
Carlo J. De Luca
Keyword(s):  
Motor Unit ◽  
Decomposition Methods ◽  
Surface Emg ◽  
Error Reduction ◽  
Motor Unit Action Potential ◽  
Reduction Algorithm ◽  
Emg Signal ◽  
Motor Unit Firing ◽  
Time Required ◽  
Semg Signals

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061 motor-unit action-potential trains (MUAPTs), obtained by decomposing surface EMG (sEMG) signals recorded during human voluntary contractions. Decomposition errors were classified into two general categories: location errors representing variability in the temporal localization of each motor-unit firing instance and identification errors consisting of falsely detected or missed firing instances. To mitigate these errors, we developed an error-reduction algorithm that combines multiple decomposition estimates to determine a more probable estimate of motor-unit firing instances with fewer errors. The performance of the algorithm is governed by a trade-off between the yield of MUAPTs obtained above a given accuracy level and the time required to perform the decomposition. When applied to a set of sEMG signals synthesized from real MUAPTs, the identification error was reduced by an average of 1.78%, improving the accuracy to 97.0%, and the location error was reduced by an average of 1.66 ms. The error-reduction algorithm in this study is not limited to any specific decomposition strategy. Rather, we propose it be used for other decomposition methods, especially when analyzing precise motor-unit firing instances, as occurs when measuring synchronization.

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