Analysis Of sEMG Signals Research Articles

Classification of Muscle Fatigue in Dynamic Contraction Using Surface Electromyography Signals and Multifractal Singularity Spectral Analysis

Muscle fatigue is a neuromuscular condition experienced during daily activities. This phenomenon is generally characterized using surface electromyography (sEMG) signals and has gained a lot of interest in the fields of clinical rehabilitation, prosthetics control, and sports medicine. sEMG signals are complex, nonstationary and also exhibit self-similarity fractal characteristics. In this work, an attempt has been made to differentiate sEMG signals in nonfatigue and fatigue conditions during dynamic contraction using multifractal analysis. sEMG signals are recorded from biceps brachii muscles of 42 healthy adult volunteers while performing curl exercise. The signals are preprocessed and segmented into nonfatigue and fatigue conditions using the first and last curls, respectively. The multifractal detrended moving average algorithm (MFDMA) is applied to both segments, and multifractal singularity spectrum (SSM) function is derived. Five conventional features are extracted from the singularity spectrum. Twenty-five new features are proposed for analyzing muscle fatigue from the multifractal spectrum. These proposed features are adopted from analysis of sEMG signals and muscle fatigue studies performed in time and frequency domain. These proposed 25 feature sets are compared with conventional five features using feature selection methods such as Wilcoxon rank sum, information gain (IG) and genetic algorithm (GA) techniques. Two classification algorithms, namely, k-nearest neighbor (k-NN) and logistic regression (LR), are explored for differentiating muscle fatigue. The results show that about 60% of the proposed features are statistically highly significant and suitable for muscle fatigue analysis. The results also show that eight proposed features ranked among the top 10 features. The classification accuracy with conventional features in dynamic contraction is 75%. This accuracy improved to 88% with k-NN-GA combination with proposed new feature set. Based on the results, it appears that the multifractal spectrum analysis with new singularity features can be used for clinical evaluation in varied neuromuscular conditions, and the proposed features can also be useful in analyzing other physiological time series.

Effect of clinical parameters on the control of myoelectric robotic prosthetic hands.

Improving the functionality of prosthetic hands with noninvasive techniques is still a challenge. Surface electromyography (sEMG) currently gives limited control capabilities; however, the application of machine learning to the analysis of sEMG signals is promising and has recently been applied in practice, but many questions still remain. In this study, we recorded the sEMG activity of the forearm of 11 male subjects with transradial amputation who were mentally performing 40 hand and wrist movements. The classification performance and the number of independent movements (defined as the subset of movements that could be distinguished with >90% accuracy) were studied in relationship to clinical parameters related to the amputation. The analysis showed that classification accuracy and the number of independent movements increased significantly with phantom limb sensation intensity, remaining forearm percentage, and temporal distance to the amputation. The classification results suggest the possibility of naturally controlling up to 11 movements of a robotic prosthetic hand with almost no training. Knowledge of the relationship between classification accuracy and clinical parameters adds new information regarding the nature of phantom limb pain as well as other clinical parameters, and it can lay the foundations for future "functional amputation" procedures in surgery.

Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals

Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p < 0.05). It appears that these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions.

Wavelet Transform-Based Classification of Electromyogram Signals Using an Anova Technique

Wavelet analysis of surface electromyogram (sEMG) signals has been investigated. Methods to remove noise before processing and further analysis are rather significant for these signals. The sEMG signals were estimated with the following steps, first, the obtained signal was decomposed using wavelet transform; then, decomposed coefficients were analyzed by threshold methods, and, finally, reconstruction was performed. Comparison of the Daubechies wavelet family for effective removing noise from the recorded sEMGs was executed preciously. As was found, wavelet transform db4 performs denoising best among the aforesaid wavelet family. Results inferred that Daubechies wavelet families (db4) were more suitable for the analysis of sEMG signals related to different upper limb motions, and a classification accuracy of 88.90% was achieved. Then, a statistical technique (one-way repeated factorial analysis) for the experimental coefficient was done to investigate the class separ ability among different motions.

The occurrence frequency: A suitable parameter for the evaluation of the myoelectric activity during walking.

Many studies have recently addressed the quantification of the natural variability of myoelectric activity during walking, considering hundreds of strides. The availability of so many strides allows assessing a parameter seldom considered in classic surface EMG (sEMG) studies: the occurrence frequency, defined as the frequency each muscle activation occurs with, quantified by the number of strides in which a muscle is recruited with that specific activation modality. Aim of this study is to point out the occurrence frequency as a suitable parameter for the evaluation of the variability of the myoelectric activity during walking. This goal was pursued by means of the statistical gait analysis of sEMG signal acquired from Gastrocnemius Lateralis (GL) in six healthy subjects, with different characteristics. Results show that among these six subjects relevant differences were not detected in the temporal parameters, i.e., activation onset/offset instant and activation duration. In the same subjects, the values of the occurrence frequency ranged from 3% to 74% in the different activation modalities, indicating a large variability of this parameter. These findings show that occurrence frequency is able to provide further and different information with respect to classical temporal parameters. Thus, the occurrence frequency is proposed as a suitable parameter to support the classic temporal parameters in the evaluation of variability of myoelectric activity during walking.

Assessment of the surface electromyographic activity of thigh muscles in males and females

Surface electromyographic (sEMG) signal is commonly used as input information to control robotic systems. sEMG signals vary from person to person; gender is a factor influencing this variation. The aim of the study is to detect gender-related differences in sEMG activity of main three thigh muscles (rectus femoris, RF, biceps femoris, BF and vastus lateralis, VL) during walking at comfortable speed and cadence. Statistical analysis of sEMG signals, performed in seven male (M-group) and seven female (F-group) adults, showed clear gender-related differences in VL behaviour and no relevant differences in the behaviour of RF and BF. F-group, indeed, adopts a walking modality with a higher number of VL activations during gait cycle, compared to M-group. This suggests a female propensity for a more complex VL recruitment, during walking. This novel information suggests considering separate approaches for males and females, in providing electromyographic signals as input information to control robotic systems.

Myofascial trigger points and innervation zone locations in upper trapezius muscles

BackgroundMyofascial trigger points (MTrPs) are hyperirritable spots located in taut bands of muscle fibres. Electrophysiological studies indicate that abnormal electrical activity is detectable near MTrPs. This phenomenon has been described as endplate noise and it has been purported to be associated MTrP pathophysiology. Thus, it is suggested that MTrPs will be overlap the innervation zone (IZ). The purpose of this work was to describe the location of MTrPs and the IZ in the right upper trapezius.MethodsWe screened 71 individuals and eventually enrolled 24 subjects with neck pain and active MTrPs and 24 neck pain-free subjects with latent MTrPs. Surface electromyography (sEMG) signals were detected using an electrode matrix during isometric contraction of the upper trapezius. A physiotherapist subsequently examined the subject’s trapezius to confirm the presence of MTrPs and establish their location. IZ locations were identified by visual analysis of sEMG signals. IZ and MTrPs locations were described using an anatomical coordinate system (ACS), with the skin area covered by the matrix divided into four quadrants.ResultsNo significant difference was observed between active and latent MTrPs locations (P = 0.6). Forty-five MTrPs were in the third quadrant of the ACS, and 3 were included in second quadrant. IZs were located approximately midway between the seventh cervical vertebrae and the acromial angle in a limited area in the second and third quadrants. The mean distance between MTrP and IZ was 10.4 ± 5.8 mm.ConclusionsAccording to the acquired results, we conclude that IZ and MTrPs are located in well-defined areas in upper trapezius muscle. Moreover, MTrPs in upper trapezius are proximally located to the IZ but not overlapped.

Reliability of surface EMG matrix in locating the innervation zone of upper trapezius muscle

The identification of the motor unit (MU) innervation zone (IZ) using surface electromyographic (sEMG) signals detected on the skin with a linear array or a matrix of electrodes has been recently proposed in the literature. However, an analysis of the reliability of this procedure and, therefore, of the suitability of the sEMG signals for this purpose has not been reported. The purpose of this work is to describe the intra and inter-rater reliability and the suitability of surface EMG in locating the innervation zone of the upper trapezius muscle. Two operators were trained on electrode matrix positioning and sEMG signal analysis. Ten healthy subjects, instructed to perform a series of isometric contractions of the upper trapezius muscle participated in the study. The two operators collected sEMG signals and then independently estimated the IZ location through visual analysis. Results showed an almost perfect agreement for intra-rater and inter-rater reliability. The constancy of IZ location could be affected by the factors reflecting the population of active MUs and their IZs, including: the contraction intensity, the acquisition period analyzed, the contraction repetition. In almost all cases the IZ location shift due to these factors did not exceed 4 mm. Results generalization to other muscles should be made with caution.