Analysis Of Myoelectric Signals Research Articles

A new approach to preprocessing of EMG signal to assess the correctness of muscle condition

Purpose: Electromyography (EMG) is a technique dealing with the recording and analysis of myoelectric signals formed by physiological variations in the muscle fiber membrane. The voltage potential of surface myoelectrical signals (sEMG) varies over time and depends on the characteristics of the individual subject. One of the main drawbacks of sEMG analysis is that the given acquisition conditions strongly determine the amplitude of the signal. The analysis of sEMG requires appropriate preprocessing, including proper filtering and artifact removal. Moreover, the sEMG data must be converted to a scale standardized for all measurements. This research aimed both to propose a method of sEMG processing to eliminate the occurring disturbances, in particular impulsive artifacts, and to determine the level of muscle excitation based on normalized sEMG. The analysis of muscle excitation level can be applied to assess muscle activity during physical activity. Design/methodology/approach: The proposed algorithm uses set of digital filters, probabilistic distribution and the decomposition of the sEMG signal to attenuate artifacts. Variance analysis of the sEMG derivative is used to determine muscle excitation. The sEMG signals were acquired with the VICON system with the sampling frequency set at 1000 Hz, and processed in MATLAB. During sEMG recordings, standard silver/silver chloride (Ag/AgCl) surface electrodes were used. Findings: The suggested technique was validated using sEMG recorded for eight persons during deep squat. Normalized excitation was determined for the left and right muscles, the rectus femoris, the vastus medialis, and the biceps femoris. Obtained outcomes indicate a possibility to assess the correctness of muscles condition. Originality/value: The combination of the proposed filter and the analysis variance-based thresholding method can effectively eliminate impulse artifacts within the surface myoelectrical signals. Keywords: Spikes disturbance, EMG, Muscle excitation, Filtering, Kurtosis. Category of the paper: Research paper.

Analysis of Muscular Paralysis using EMG Signal with Wavelet Decomposition Approach

Paralysis refers to temporary or permanent loss of voluntary muscle movement in a body part or region. The degree of muscle function loss determines the severity of paralysis. The muscle function is represented by electrical activity of the muscles. Electromyography is a technique concerned with the analysis of myoelectric signals. EMG allows the determination of muscular activity. EMG signal analysis is performed using the features extracted in time domain, frequency domain and time frequency domain. In this work, the EMG of Amyotrophic Lateral Sclerosis (ALS), Myopathy, and Normal conditions are considered, and the time frequency analysis has been carried out to extract the features using wavelet decomposition approach. The classification of normal and paralyzed condition is carried by four classifier models. The classifier models used are Multi-layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and Nearest Neighbor (NN) models. The standard data set has been used for the purpose. The classification accuracy obtained for MLP is 80%, for RF is 75%, for GB is 79%, and for NN is 69%. MLP show better classification performance over RF, GB, and NN Classifiers.

Analysis of Myoelectric Signals to Prosthesis Applications

This work presents a myoelectric system measurement incorporated in an arm band device, particularly designed for application in upper-limb myoelectric prosthesis with pattern recognition-based sEMG control. For this particular use, specifications such as low cost, high sampling frequency, size and weight, may be observed. Moreover, it is introduced an acquisition protocol to evaluation of the myoelectric signal in daily activities.

Functional evaluation of triceps surae during heel rise test: from EMG frequency analysis to machine learning approach.

Soleus muscle flap as coverage tissue is a possible surgical solution adopted to cover the wounds due to open fractures. Despite this procedure presents many clinical advantages, relatively poor information is available about the loss of functionality of triceps surae of the treated leg. In this study, a group of patients who underwent a soleus muscle flap surgical procedure has been analyzed through the heel rise test (HRT), in order to explore the triceps surae residual functionalities. A frequency band analysis was performed in order to assess whether the residual heads of triceps surae exhibit different characteristics with respect to both the non-treated lower limb and an age-matched control group. Then, an in-depth analysis based on a machine learning approach was proposed for discriminating between groups by generalizing across new unseen subjects. Experimental results showed the reliability of the proposed analyses for discriminating between-group at a specific time epoch and the high interpretability of the proposed machine learning algorithm allowed the temporal localization of the most discriminative frequency bands. Findings of this study highlighted that significant differences can be recognized in the myoelectric spectral characteristics between the treated and contralateral leg in patients who underwent soleus flap surgery. These experimental results may support the clinical decision-making for assessing triceps surae performance and for supporting the choice of treatment in plastic and reconstructive surgery. Graphical Abstract The Graphical abstract presents the scope of the proposed analysis of myoelectric signals of soleus and gastrocnemius muscles of patiens groups during Hell Rise Test, highlighting the applied methods and the obtained results.

Time and Frequency Patterns Identification of sEMG Signals Using Hilbert-Huang Transform

This article reports the identification of two groups of patterns in time and frequency, also a muscular intensity characteristic associated to hand movement per user, all this results were obtained by superficial myoelectric signal analysis using Hilbert-Huang transform. Muscular signals were acquired from five muscles of the right forearm from five healthy subjects. Using a four channels acquisition system with differential configuration myoelectric signals were recorded for six movements of the fingers in flexion and extension. Electrodes were placed over five forearm anterior, posterior and finger linked muscles. Time pattern corresponds to sinusoidal oscillations with average length of 24.5 ms, corresponding to voluntary contraction and relaxation of the muscles. These oscillations are present into all acquisition channels and there is evidence about groups of two consecutive oscillations with a range of 30 ms between them. Also, there found a pattern of three frequencies: 83.3 Hz, 96.7 Hz and 113.3 Hz, present into contraction and relaxation timeslot. Using Hilbert transform were identified the instantaneous frequencies that indicate the state of the muscles from doss to active and vice versa. There is an intensity characteristic identified in the recordings about behavior of muscles per movement for each subject being repetitive and unique. These processes could decrease time computing to obtain intrinsic characteristics for myoelectric signals.

Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals

In this investigation, extracted features ofsignals have been analyzed for the recognition of arm movements. Short-time Fourier transform and wavelet transform based on Euclidian distance were applied to reordered signals. Results show that wavelet is a more useful and powerful tool for analyzing signals, since it shows multiresolution property with a significant reduction in the computation time for eliminating resolution problems. Finally, a statistical technique of repeated factorial analysis of variance for experimental recorded data was implemented in a way to investigate the effect of class separability for multiple motions for establishing surface electromyogram–muscular force relationship.

PREDICTION OF ABOVE-ELBOW MOTIONS IN AMPUTEES, BASED ON ELECTROMYOGRAPHIC(EMG) SIGNALS, USING NONLINEAR AUTOREGRESSIVE EXOGENOUS (NARX) MODEL

Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG), as an experimental technique,is concerned with the development, recording, and analysis of myoelectric signals. EMG-based research is making progress in the development of simple, robust, user-friendly, and efficient interface devices for the amputees. Materials and Methods Prediction of muscular activity and motion patterns is a common, practical problem in prosthetic organs. Recurrent neural network (RNN) models are not only applicable for the prediction of time series, but are also commonly used for the control of dynamical systems. The prediction can be assimilated to identification of a dynamic process. An architectural approach of RNN with embedded memory is Nonlinear Autoregressive Exogenous (NARX) model, which seems to be suitable for dynamic system applications. Results Performance of NARX model is verified for several chaotic time series, which are applied as input for the neural network. The results showed that NARX has the potential to capture the model of nonlinear dynamic systems. The R-value and MSE are and , respectively. Conclusion EMG signals of deltoid and pectoralis major muscles are the inputs of the NARX network. It is possible to obtain EMG signals of muscles in other arm motions to predict the lost functions of the absent arm in above-elbow amputees, using NARX model.

Surface Myoelectric Signal Analysis: Dynamic Approaches for Change Detection and Classification

Toward the goal of elbow and wrist prostheses control by characterizing events in surface myoelectric signals, this paper presents a dynamic method to simultaneously detect and classify such events. Dynamic cumulative sum of local generalized likelihood ratios using wavelet decomposition of the myoelectric signal is used for on-line detection. Frequency as well as energy changes are detected with this hybrid approach. Classification is composed of using multiresolution wavelet analysis and autoregressive modeling to extract signal features while polynomial classifiers are used for pattern modeling and matching. The results of detecting and classifying four elbow and wrist movements show that, in average, 91% of the events are correctly detected and classified using features obtained from multiresolution wavelet analysis while 95% accuracy is achieved with AR modeling. The classification accuracy decreases, however, if short prostheses response delay is desired. This paper also shows that the performance of the polynomial classifiers is better than that of the commonly used neural networks since it gives higher classification accuracy and consistent classification outcomes. In comparison to the well known support vector machine classification, the polynomial classifier gives similar results without the need to optimize and search for classifier parameters.

Physiological determinates of the non-stationary events resolved by wavelet analysis of myoelectric signals

Physiological determinates of the non-stationary events resolved by wavelet analysis of myoelectric signals

Clinical applications of surface electromyography in neuromuscular disorders

Surface electromyography (SEMG) is still rarely used in clinical settings for the detection and analysis of myoelectric signals. The electromyographic signal detected on the skin surface includes information from a greater proportion of the muscle of interest than conventional clinical EMG, acquired using needle electrodes. SEMG is therefore more representative than the localised, and thus very selective needle EMG signals currently used. However, both reliability and interpretation of surface EMG need to be questioned. This review looks at the studies concerned with the characterisation of neuromuscular pathologies using EMG parameters. After introducing principles and limitations of surface EMG, an overview of the main results obtained in clinical settings is presented and discussed. There is a particular focus on high spatial resolution surface EMG as it is currently the best compromise between the selectivity of needle EMG and the representative nature of classical SEMG. Several perspectives are proposed that underline the fact that surface EMG is an evolving discipline and should be worthy of a place in routine clinical examinations.

Intensity analysis in time-frequency space of surface myoelectric signals by wavelets of specified resolution

Surface myoelectric signals often appear to carry more information than what is resolved in root mean square analysis of the progress curves or in its power spectrum. Time-frequency analysis of myoelectric signals has not yet led to satisfactory results in respect of separating simultaneous events in time and frequency. In this study a time-frequency analysis of the intensities in time series was developed. This intensity analysis uses a filter bank of non-linearly scaled wavelets with specified time-resolution to extract time-frequency aspects of the signal. Special procedures were developed to calculate intensity in such a way as to approximate the power of the signal in time. Applied to an EMG signal the intensity analysis was called a functional EMG analysis. The method resolves events within the EMG signal. The time when the events occur and their intensity and frequency distribution are well resolved in the intensity patterns extracted from the EMG signal. Averaging intensity patterns from multiple experiments resolve repeatable functional aspects of muscle activation. Various properties of the functional EMG analysis were shown and discussed using model EMG data and real EMG data.

Propagation velocity of muscle action potentials in the growing normal child

Power spectrum analysis of myoelectric signals has been used to measure the propagation velocity in a large number of voluntary action potentials in biceps and brachioradialis muscles of normal children, 0-20 years old. The method used can be applied to both single motor units signals and interference electromyogram. From measurements of single motor units, the distribution of potential velocities in the muscle can be obtained, which makes conclusive statements on the development of the velocity during childhood more reliable. The propagation velocities are found to increase with age, body height, and muscle diameter. The dependencies on these size parameters are close to that of the square root function, in accordance with electrophysical theory.

Analysis of myoelectric signals recorded during dynamic contractions

This article presents the theoretical considerations that justify the choice of specific time-frequency transforms for processing nonstationary myoelectric signals as a method of studying fatigue prior to the failure point. It shows some preliminary results obtained by applying these techniques to computer-synthesized realizations of stochastic processes, as well as to real signals detected during different types of dynamic contractions of healthy human volunteers. Five different time-frequency transforms were applied in this study (the Wigner-Ville, the smoothed Wigner-Ville, the Cone kernel, the reduced interference, and the Choi-Williams), but for the sake of brevity, this article reports only the results obtained by applying the Choi-Williams transform, because the authors found it to be the most suitable for processing these specific signals.

Advances in processing of surface myoelectric signals: Part 2

The paper focuses on the analysis of myoelectric evoked potentials and their progressive scaling, as well as morphological changes using orthogonal basis functions with essentially finite time support; and the compression of the information content using principal component analysis. The application of the Hermite-Rodriguez and the associated Hermite functions is discussed as a means to provide compact information about the shape of the M-wave or of the power spectral density function of either voluntary or electrically elicited myoelectric signals; a means to estimate scaling factors; and a means to describe and classify nonstationarities. The principal component analysis shows the possibility of a compression ratio of at least 10:1 in the storage of M-wave sequences. The paper also describes three methods for the estimation of delay between similar signals, and therefore for estimation of conduction velocity. They are based on normalised integrals. Fourier transform matching and matching in the time domain. In particular conditions they provide different results for the same pair of signals. The concept of delay and the performance of these methods are reviewed and discussed. The paper is not exhaustive. It has the main objective of making the reader aware of the wealth of methods available for nonstationary myoelectric signal analysis and conduction velocity estimation, and of the need to use them with knowledge of their respective advantages, disadvantages, peculiarities and limitations.

Single motor unit myoelectric signal analysis with nonstationary data.

The information content of the myoelectric signal (MES) is commonly revealed by statistical measures in the time or frequency domain. Empirical analyses of the MES from a single motor unit have generally assumed that features are invariant with time. Theoretical and experimental work has been done to demonstrate how nonstationary behavior in the discharge statistics of a motor neuron may affect estimates of features extracted from the motor unit's contribution to the MES. Specifically, it has been shown that nonstationary behavior can markedly influence estimates of features describing motor neuron firing behavior and consequently, the low-frequency portion of the MES power spectral density. These results may help to explain the discrepancies in the literature which report empirical models of motor neuron firing statistics.

Computer analysis of the myoelectric signal

The basics of myoelectric signals are reviewed. Surface myoelectric signal analysis is discussed, focusing on processing techniques. Two needle electromyography techniques are described to show typical requirements for a computer system used in this field. The use of expert systems is briefly considered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The application of neural networks to myoelectric signal analysis: a preliminary study

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

Myoelectric signal analysis using neural networks

It is shown that the capacity of a discrete Hopfield network for functional minimization allows it to extract the time-series parameters from a myoelectric signal (MES) at a faster rate than the previously used SLS algorithm. With a two-dimensional signal space consisting of one of the parameters and the signal power, a two-layer perceptron trained using back-propagation has been used to classify MES signals from different types of muscular contractions. The results suggest that neural networks may be suitable for MES analysis tasks and that further research in this direction is warranted.

New techniques for surface myoelectric signal analysis: [formula omitted] (Dept. of Electronics, Politecnico di Torino, Turin, Italy)

New techniques for surface myoelectric signal analysis: [formula omitted] (Dept. of Electronics, Politecnico di Torino, Turin, Italy)

Masseter muscle fatigue in man objectively quantified by analysis of myoelectric signals

Localized muscle fatigue in the masseter muscle was studied with a method based on power spectrum analysis of myoelectric signals. Under the influence of fatiguing contractions, a gradual shift of the spectral curve occurred; the rate of change was taken as a measure of the development of fatigue. The fatigue was dependent on the bite force. The existence of a threshold value of force, below which significant myoelectric fatigue changes do not develop, was shown.