scholarly journals Multichannel Surface EMG Decomposition Based on Measurement Correlation and LMMSE

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yong Ning ◽  
Yuming Zhao ◽  
Akbarjon Juraboev ◽  
Ping Tan ◽  
Jin Ding ◽  
...  
Keyword(s):  
Minimum Mean Square Error ◽  
Large Population ◽  
Electrode Array ◽  
Motor Units ◽  
True Positive Rate ◽  
Surface Emg ◽  
Semg Signal ◽  
Positive Rate ◽  
Channel Electrode ◽  
Semg Signals

A method based on measurement correlation (MC) and linear minimum mean square error (LMMSE) for multichannel surface electromyography (sEMG) signal decomposition was developed in this study. This MC-LMMSE method gradually and iteratively increases the correlation between an optimized vector and a reconstructed matrix that is correlated with the measurement matrix. The performance of the proposed MC-LMMSE method was evaluated with both simulated and experimental sEMG signals. Simulation results show that the MC-LMMSE method can successfully reconstruct up to 53 innervation pulse trains with a true positive rate greater than 95%. The performance of the MC-LMMSE method was also evaluated using experimental sEMG signals collected with a 64-channel electrode array from the first dorsal interosseous muscles of three subjects at different contraction levels. A maximum of 16 motor units were successfully extracted from these multichannel experimental sEMG signals. The performance of the MC-LMMSE method was further evaluated with multichannel experimental sEMG data by using the “two sources” method. The large population of common MUs extracted from the two independent subgroups of sEMG signals demonstrates the reliability of the MC-LMMSE method in multichannel sEMG decomposition.

scholarly journals Decomposition of surface EMG signals from cyclic dynamic contractions

2015 ◽  
Vol 113 (6) ◽  
pp. 1941-1951 ◽  
Author(s):  
Carlo J. De Luca ◽  
Shey-Sheen Chang ◽  
Serge H. Roy ◽  
Joshua C. Kline ◽  
S. Hamid Nawab
Keyword(s):  
Motor Units ◽  
Surface Emg ◽  
Machine Learning Algorithms ◽  
Isometric Contractions ◽  
Semg Signal ◽  
Emg Signal ◽  
Verification Methods ◽  
Flexion Extension ◽  
Dynamic Contractions ◽  
Cyclic Contractions

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.

scholarly journals Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

2019 ◽  
Vol 11 (1) ◽  
pp. 25 ◽  
Author(s):  
Osama Dorgham ◽  
Ibrahim Al-Mherat ◽  
Jawdat Al-Shaer ◽  
Sulieman Bani-Ahmad ◽  
Stephen Laycock
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Potential ◽  
Eccentric Contraction ◽  
Surface Emg ◽  
Mean Square ◽  
Ann Model ◽  
Semg Signal ◽  
Artificial Neural ◽  
Semg Signals

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

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 A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal

2022 ◽  
Vol 12 (2) ◽  
pp. 1530
Author(s):  
Ayad Assad Ibrahim ◽  
Ikhlas Mahmoud Farhan ◽  
Mohammed Ehasn Safi
Keyword(s):  
Surface Electromyography ◽  
Spatial Interpolation ◽  
Goodness Of Fit ◽  
Electrode Array ◽  
Motor Units ◽  
Biomedical Signal Processing ◽  
Semg Signal ◽  
Distance Weighted ◽  
A New Technique ◽  
Inverse Distance

Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.

A Multichannel Surface EMG System for Hand Motion Recognition

2015 ◽  
Vol 12 (02) ◽  
pp. 1550011 ◽  
Author(s):  
Yinfeng Fang ◽  
Honghai Liu ◽  
Gongfa Li ◽  
Xiangyang Zhu
Keyword(s):  
Root Mean Square ◽  
Surface Emg ◽  
Signal Acquisition ◽  
Mean Square ◽  
Semg Signal ◽  
Motion Recognition ◽  
Hand Motion ◽  
Prosthetic Hands ◽  
The Relationship ◽  
Semg Signals

Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.

Classification of Muscular Nonfatigue and Fatigue Conditions Using Surface EMG Signals and Fractal Algorithms

2016 ◽  
Author(s):  
Kiran Marri ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Muscle Fatigue ◽  
Fractal Analysis ◽  
Sports Medicine ◽  
Biceps Brachii ◽  
Moving Average ◽  
Surface Emg ◽  
Feature Subset ◽  
Scale Invariant ◽  
Semg Signal ◽  
Semg Signals

The application of surface electromyography (sEMG) technique for muscle fatigue studies is gaining importance in the field of clinical rehabilitation and sports medicine. These sEMG signals are highly nonstationary and exhibit scale-invariant self-similarity structure. The fractal analysis can estimate the scale invariance in the form of fractal dimension (FD) using monofractal (global single FD) or multifractal (local varying FD) algorithms. A comprehensive study of sEMG signal for muscle fatigue using both multifractal and monofractal FD features have not been established in the literature. In this work, an attempt has been made to differentiate sEMG signals recorded nonfatigue and fatigue conditions using monofractal and multifractal algorithms, and machine learning methods. For this purpose, sEMG signals have been recorded from biceps brachii muscles of fifty eight healthy subjects using a standard protocol. The signals of nonfatigue and fatigue region were subjected to eight monofractal (Higuchi, Katz, Petrosian, Sevcik, box counting, multi-resolution length, Hurst and power spectrum density) and two multifractal (detrended fluctuating and detrended moving average) algorithms and 28 FD features were extracted. The features were ranked using conventional and genetic algorithms, and a subset of FD features were further subjected to Naïve Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP) classifiers. The results show that all fractal features are statistically significant. The classification accuracy using feature subset of conventional method is observed to be from 83% to 88%. The highest accuracy of 93.96% was achieved using genetic algorithm and LR classifier combination. The result demonstrated that the performance of multifractal FD features to be more suitable for sEMG signals as compared to monofractal FD features. The fractal analysis of sEMG signals appears to be a very promising biomarker for muscle fatigue classification and can be extended to detection of fatigue onset in varied neuromuscular conditions.

Decomposition of Surface EMG Signals

2006 ◽  
Vol 96 (3) ◽  
pp. 1646-1657 ◽  
Author(s):  
Carlo J. De Luca ◽  
Alexander Adam ◽  
Robert Wotiz ◽  
L. Donald Gilmore ◽  
S. Hamid Nawab
Keyword(s):  
Action Potential ◽  
Action Potentials ◽  
Sensor Array ◽  
Cranial Nerves ◽  
Surface Emg ◽  
Semg Signal ◽  
Orbicularis Oculi ◽  
Knowledge Based ◽  
Automatic Mode ◽  
Semg Signals

This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence framework. In the automatic mode the accuracy ranges from 75 to 91%. An Interactive Editor is used to increase the accuracy to >97% in signal epochs of about 30-s duration. The accuracy was verified by comparing the firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have decomposed up to six MU action potential trains from the sEMG signal detected from the orbicularis oculi, platysma, and tibialis anterior muscles. However, the yield is generally low, with typically ≤5 MUs per contraction. Both the accuracy and the yield should increase as the algorithms are developed further. With this technique it is possible to investigate the behavior of MUs in muscles that are not easily studied by needle sensors. We found that the inverse relationship between the recruitment threshold and the firing rate previously reported for muscles innervated by spinal nerves is also present in the orbicularis oculi and the platysma, which are innervated by cranial nerves. However, these two muscles were found to have greater and more widespread values of firing rates than those of large limb muscles.

Amplitude cancellation reduces the size of motor unit potentials averaged from the surface EMG

2006 ◽  
Vol 100 (6) ◽  
pp. 1928-1937 ◽  
Author(s):  
Kevin G. Keenan ◽  
Dario Farina ◽  
Roberto Merletti ◽  
Roger M. Enoka
Keyword(s):  
Motor Unit ◽  
Conduction Velocity ◽  
Motor Units ◽  
Surface Emg ◽  
Excitation Level ◽  
Bipolar Electrode ◽  
Emg Signal ◽  
Motor Unit Synchronization ◽  
Rate Coding ◽  
Simulated Surface

The purpose of the study was to evaluate the influence of selected physiological parameters on amplitude cancellation in the simulated surface electromyogram (EMG) and the consequences for spike-triggered averages of motor unit potentials derived from the interference and rectified EMG signals. The surface EMG was simulated from prescribed recruitment and rate coding characteristics of a motor unit population. The potentials of the motor units were detected on the skin over a hand muscle with a bipolar electrode configuration. Averages derived from the EMG signal were generated using the discharge times for each of the 24 motor units with lowest recruitment thresholds from a population of 120 across three conditions: 1) excitation level; 2) motor unit conduction velocity; and 3) motor unit synchronization. The area of the surface-detected potential was compared with potentials averaged from the interference, rectified, and no-cancellation EMGs. The no-cancellation EMG comprised motor unit potentials that were rectified before they were summed, thereby preventing cancellation between the opposite phases of the potentials. The percent decrease in area of potentials extracted from the rectified EMG was linearly related to the amount of amplitude cancellation in the interference EMG signal, with the amount of cancellation influenced by variation in excitation level and motor unit conduction velocity. Motor unit synchronization increased potentials derived from both the rectified and interference EMG signals, although cancellation limited the increase in area for both potentials. These findings document the influence of amplitude cancellation on motor unit potentials averaged from the surface EMG and the consequences for using the procedure to characterize motor unit properties.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

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