Electromyography Signals Research Articles

Neuromuscular Adaptations in Response to Disuse and Weight-Training Rehabilitation are Enhanced in Resistance Trained Individuals

INTRODUCTION: Lower-limb disuse from immobilization results in a decrease in muscle strength, volume, and function. However, muscle atrophy from disuse does not entirely account for the observed strength and functional declines. Additionally, prior engagement in resistance training (RT) has been linked to the preservation of muscular strength and neuromuscular function. Therefore, the purpose of this study was to assess the influence of a single-leg immobilization protocol, followed by structured RT rehabilitation protocol, on muscle strength, volume, and neural activation in healthy young adults with and without previous strength training experience. Methods: Participants (10 Trained (T): 8 men, 2 women; 11 Untrained (UT): 8 men, 3 women) underwent 2-weeks of left-knee immobilization at 90° knee-flexion with a hinged immobilizer knee-brace. Participants were given a pair of axillary crutches for ambulation during this time. Following immobilization, all participants underwent an 8-week structured RT rehabilitation protocol. Participants visited the laboratory for three testing sessions (baseline test on Day 0; post-immobilization at the end of week 2; and post-RT rehab at the end of week 10). Maximal knee extensor isometric strength at 45°was quantified using an isokinetic dynamometer (Biodex). Voluntary muscle activation was quantified as the root mean square (RMS) of the interference electromyography (EMG) signals derived from two indwelling fine-wire electrodes in the vastus-lateralis muscle. Additionally, a continuous wavelet transform (CWT) was performed on the EMG to determine amplitude versus frequency characteristics of the signals and how this amplitude varied with time in the 13-30 (beta) and 30-60 (piper) Hz bands. Mid-thigh muscle cross-sectional area (mCSA) and density were also quantified using peripheral quantitative computed tomography (pQCT) during all testing sessions. Results: The T group was significantly stronger (p=0.019), had greater muscle density (p=0.005) and mid-thigh mCSA (p<0.001) across all sessions when compared with the UT group. However, there were no significant time or interaction effects for strength or muscle density losses (p>0.05). While both groups demonstrated increased muscle activation across sessions (RMS: p=0.04; CWT: p=0.021), the T group demonstrated a greater increase in the neural drive from baseline to week 10 when compared with the UT group (RMS: p= 0.002; CWT: p=0.027). CONCLUSION: In younger adults with or without prior strength training experience, we find that losses in knee extensor isometric strength, muscle density, and mid-thigh mCSA are minimal following a 2-week immobilization protocol. However, certain neuromuscular adaptations during immobilization and following weight-training rehabilitation are enhanced to a greater degree in those with prior training, which is seemingly indicative of greater synchrony in descending cortical drive and motor unit recruitment. These latter findings warrant continued investigation using additional innovative methodologies (e.g., fMRI, TMS, etc.) for assessing the mechanisms underlying the effects of prehab protocols on neuromuscular adaptations. PGRF and Nix family foundation scholarship to Marina Meyer. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation.

The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand position with high accuracy and computational efficiency. RPC-Net uses a regression-based approach to convert forearm electromyographic signals into hand kinematics. We tested the adaptability of the algorithm to different conditions and compared its performance with that of solutions from the academic literature. RPC-Net demonstrated a high degree of accuracy in predicting hand position from electromyographic activity, outperforming other solutions with the same computational cost. Including previous position data consistently improved results across subjects and conditions. RPC-Net showed robustness against a reduction in the number of electromyography electrodes used and shorter input signals, indicating potential for further reduction in computational cost. The results demonstrate that RPC-Net is capable of accurately translating forearm electromyographic activity into hand position, offering a practical and adaptable tool that may be accessible in clinical settings. The development of RPC-Net represents a significant advancement. In clinical settings, its application could enable prosthetic devices to be controlled in a way that feels more natural, improving the quality of life for individuals with limb loss.

Online Muscle Activation Onset Detection Using Likelihood of Conditional Heteroskedasticity of Electromyography Signals.

The real-time, accurate detection of muscle activation onset (MAO) is significant for EMG-triggered control strategies in embedded applications like prostheses and exoskeletons. This paper investigates sEMG signals using the generalized autoregressive conditional heteroskedasticity (GARCH) model, focusing on variance. A novel feature, the likelihood of conditional heteroskedasticity (LCH) extracted from the maximum likelihood estimation of GARCH parameters, is proposed. This feature effectively distinguishes signal from noise based on heteroskedasticity, allowing for the detection of MAO through the LCH feature and a basic threshold classifier. For online calculation, the model parameter estimation is simplified, enabling direct calculation of the LCH value using fixed parameters. The proposed method was validated on two open-source datasets and demonstrated superior performance over existing methods. The mean absolute error of onset detection, compared with visual detection results, is approximately 65 ms under online conditions, showcasing high accuracy, universality, and noise insensitivity. The results indicate that the proposed method using the LCH feature from the GARCH model is highly effective for real-time detection of muscle activation onset in sEMG signals. This novel approach shows great potential and possibility for real-world applications, reflecting its superior performance in accuracy, universality, and insensitivity to noise.

Spectral characterization of human leg EMG signals from an open access dataset for the development of computational models.

Large-scale neuromusculoskeletal models have been used for predicting mechanisms underlying neuromuscular functions in humans. Simulations of such models provide several types of signals of practical interest, such as surface electromyographic signals (EMG), which are compared with experimental data for interpretations of neurophysiological phenomena under study. Specifically, realistic characterization of spectral properties of simulated EMG signals is important for achieving powerful inferences, whereas considerations should be taken for myoelectric signals of different muscles. In this study, we characterized spectral properties of surface interference pattern EMG signals and motor unit action potentials (MUAP) acquired from three plantar flexor muscles: Soleus (SO), Medial Gastrocnemius (MG), and Lateral Gastrocnemius (LG); and one dorsiflexor muscle: Tibialis Anterior (TA). Surface EMG signals were acquired from 20 participants using the same convention for electrode placement. Specifically, interference pattern EMG signals were obtained during isometric constant force contractions at 5%, 10% and 20% of maximum voluntary contraction (MVC), whereas surface MUAPs were decomposed from surface EMG signals obtained at low contraction forces. We compared the spectrum median frequency (MDF) estimated from interference pattern EMG signals across muscles and contraction intensities. Additionally, we compared MDF and durations of MUAPs between muscles. Our results showed that MDF of interference pattern EMG signals acquired from TA were higher compared to SO, MG, and LG for all contraction intensities i.e., 5%, 10%, and 20% MVC. Consistently, MUAPs acquired from TA also had higher MDF values and shorter durations compared to the other leg muscles. We provide herein a dataset with the surface MUAPs waveforms and interference pattern EMG signals obtained for this study, which should be useful for implementing and validating the simulation of myoelectrical signals of leg muscles. Importantly, these results indicate that spectral properties of myoelectrical signals should be considered for improving EMG modeling in large-scale neuromusculoskeletal models.

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals.

This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices.

Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force-Tempo Variations.

Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate the impact of aging and motor expertise on these components, and better understand the nervous system's adaptions to varying task demands. We utilized a rectified latent variable model (RLVM) to factorize motor modules with inhibitory components from EMG signals recorded from ten expert pianists when they played scales and pieces at different tempo-force combinations. We found that older participants showed a higher proportion of inhibitory components compared with the younger group. Senior experts had a higher proportion of inhibitory components on the left hand, and most inhibitory components became less negative with increased tempo or decreased force. Our results demonstrated that the inhibitory components in muscle synergies could be shaped by aging and expertise, and also took part in motor control for adapting to different conditions in complex tasks.

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems.

Nonlinear dynamics of postural control system under visual-vestibular habituation balance practice: evidence from EEG, EMG and center of pressure signals.

Human postural control system is inherently complex with nonlinear interaction among multiple subsystems. Accordingly, such postural control system has the flexibility in adaptation to complex environments. Previous studies applied complexity-based methods to analyze center of pressure (COP) to explore nonlinear dynamics of postural sway under changing environments, but direct evidence from central nervous system or muscular system is limited in the existing literature. Therefore, we assessed the fractal dimension of COP, surface electromyographic (sEMG) and electroencephalogram (EEG) signals under visual-vestibular habituation balance practice. We combined a rotating platform and a virtual reality headset to present visual-vestibular congruent or incongruent conditions. We asked participants to undergo repeated exposure to either congruent (n = 14) or incongruent condition (n = 13) five times while maintaining balance. We found repeated practice under both congruent and incongruent conditions increased the complexity of high-frequency (0.5-20 Hz) component of COP data and the complexity of sEMG data from tibialis anterior muscle. In contrast, repeated practice under conflicts decreased the complexity of low-frequency (<0.5 Hz) component of COP data and the complexity of EEG data of parietal and occipital lobes, while repeated practice under congruent environment decreased the complexity of EEG data of parietal and temporal lobes. These results suggested nonlinear dynamics of cortical activity differed after balance practice under congruent and incongruent environments. Also, we found a positive correlation (1) between the complexity of high-frequency component of COP and the complexity of sEMG signals from calf muscles, and (2) between the complexity of low-frequency component of COP and the complexity of EEG signals. These results suggested the low- or high-component of COP might be related to central or muscular adjustment of postural control, respectively.

The phenomenon of anticipation in fencing. An applicability approach.

The aim of the study was to determine the structure of muscular activity and ground reaction forces during the preparatory period and the execution of a fencing lunge at the opponent's torso. The analysis focused on the correlations between three phases of a fencing technical action in the context of factors of temporal anticipation. Six female épée fencers from the Polish National Fencing Team participated in the study. The research tools included electromyography (EMG), ground reaction force (GRF) platforms, and the OptiTrack motion capture system. The fencers performed the lunge three times in response to visual cues from the coach. By integrating the testing system, the EMG signal indices of the fencers' upper and lower limbs and the vertical force values of the fencers' front and rear leg muscles were obtained simultaneously. The results of the study demonstrated the key role of five muscles: BICEPS BRACHII, LAT TRICEPS, EXTCARP RAD, BICEPS FEMORIS and MED GAS in influencing the speed of lunge execution. In addition, a significant correlation was found between the EMG signal of the gastrocnemius muscle of the rear leg and the movement time (MT) phase of the lunge execution. The anticipatory activation of the EMG signal in relation to the vertical force waveforms generated by the ground forces response platform in the 15-30 ms interval was demonstrated. Finally, the importance of the preparatory period for the effectiveness of the fencing lunge was highlighted based on the phenomenon of anticipation.

An On‐Skin‐Formed Silk Protein Bioelectrode for Conformable and Robust Electrophysiological Interface

AbstractThe electrophysiological interface is essential for understanding electrical activities within biological organisms and human‐machine interaction. However, traditional preformed bioelectrodes show insufficient interface contact with hairy and irregular skin, causing signal attenuation and distortion. Here, an on‐skin‐formed silk protein bioelectrode is reported with a conformable and robust interface, achieving a high signal‐to‐noise ratio for electromyographic signals, showing up to a 38.9% increase compared to traditional ones. The fluid‐gel transition of silk protein is induced by a gelling agent composed of ethanol and sodium alginate, leading to a conformable and seamless interface with hairy and irregular human skin, thus resulting in a high signal‐to‐noise ratio. The developed bioelectrode can record electrophysiological signals stably, even underwater. Additionally, human‐machine interaction using electromyographic signals is designed to precisely control the model car's motion trajectory. This work presents an effective method for developing a seamless and conformable electrophysiological interface in bioelectronics.

A reusable, healable, and biocompatible PEDOT:PSS hydrogel-based electrical bioadhesive interface for high-resolution electromyography monitoring and time–frequency analysis

As the most ideal interface between bioelectronic devices and biological tissues, the electrical bioadhesive interface (EBI) holds significant potential for various applications, including soft bioelectronics, wearable devices, and electrophysiological signal monitoring. However, most current EBIs are disposable products that lack repeatable adhesion and self-healing properties, thus hindering practical applications. Herein, we develop a reusable EBI by incorporating a dynamic non-covalent interaction enhancer polydopamine (PDA) and conductive PEDOT:PSS into an adhesive hydrogel matrix polyacrylamide (PAM). The EBI exhibits intriguing overall electrical and mechanical properties, including high conductivity (5.57 S m−1), tissue-like Young’s modulus (2.7 kPa), low interfacial impedance (1 kΩ), and excellent adhesion strength (46.45 ± 1.75 kPa on porcine skin) with excellent retention (87 %) after 200 adhering/detaching cycles, high self-healing efficiency of 94.11 %, and outstanding biocompatibility in HDF-a and HaCaT cell models. Benefiting from these superior properties, we further demonstrate the fabrication and application of an EBI-based reusable electromyography monitoring patch (EMP). The reusable EMP enables high-resolution electromyography signal monitoring capability with a higher signal-to-noise ratio (SNR) of 30.05 dB than commercial electrodes (12.06 dB). Remarkably, such reusable EMP maintains very stable SNR of 21.82 dB after 200 adhering/detaching cycles (3.8 times higher than commercial electrodes, 5.74 dB after 30 cycles), and showcases typical time and frequency-domain characteristics. These findings demonstrate a promising reusable EMP to enhance the accuracy and convenience of electromyography monitoring.

The comparison and processing of Electromyography (EMG) signals under different actions

The experiment first investigates the working principle and the applications of Electromyography (EMG). Then, the effect of electrode location and electrode size on the EMG signal is explored by the raw data and theory. The reasons for choosing generic surface Ag/AgCl adhesive electrodes are also described. After obtaining the raw EMG signals, suitable data processing methods, such as Root Mean Square (RMS) and digital filtering are applied to generate signals that can drive the motor of a prosthetic arm. The results obtained by the two methods are also compared, where RMS can provide larger peak and mean values, and the digital filtering method can minimise the phase shift. Finally, the limitations of the EMG for prostheses are also analysed by three aspects: different flexion forces, individual finger motion and different reference locations.

Identification of the Biomechanical Response of the Muscles That Contract the Most during Disfluencies in Stuttered Speech.

Stuttering, affecting approximately 1% of the global population, is a complex speech disorder significantly impacting individuals' quality of life. Prior studies using electromyography (EMG) to examine orofacial muscle activity in stuttering have presented mixed results, highlighting the variability in neuromuscular responses during stuttering episodes. Fifty-five participants with stuttering and 30 individuals without stuttering, aged between 18 and 40, participated in the study. EMG signals from five facial and cervical muscles were recorded during speech tasks and analyzed for mean amplitude and frequency activity in the 5-15 Hz range to identify significant differences. Upon analysis of the 5-15 Hz frequency range, a higher average amplitude was observed in the zygomaticus major muscle for participants while stuttering (p < 0.05). Additionally, when assessing the overall EMG signal amplitude, a higher average amplitude was observed in samples obtained from disfluencies in participants who did not stutter, particularly in the depressor anguli oris muscle (p < 0.05). Significant differences in muscle activity were observed between the two groups, particularly in the depressor anguli oris and zygomaticus major muscles. These results suggest that the underlying neuromuscular mechanisms of stuttering might involve subtle aspects of timing and coordination in muscle activation. Therefore, these findings may contribute to the field of biosensors by providing valuable perspectives on neuromuscular mechanisms and the relevance of electromyography in stuttering research. Further research in this area has the potential to advance the development of biosensor technology for language-related applications and therapeutic interventions in stuttering.

Bichectomy Surgery and EMG Masticatory Muscles Function in Adult Women: A Longitudinal Study

Bichectomy Surgery and EMG Masticatory Muscles Function in Adult Women: A Longitudinal Study

Enhancing Robustness of Surface Electromyography Pattern Recognition at Different Arm Positions for Transhumeral Amputees Using Deep Adversarial Inception Domain Adaptation

Pattern recognition in myoelectric control that relies on the myoelectric activity associated with arm motions is an effective control method applied to myoelectric prostheses. Individuals with transhumeral amputation face significant challenges in effectively controlling their prosthetics, as muscle activation varies with changes in arm positions, leading to a notable decrease in the accuracy of motion pattern recognition and consequently resulting in a high rejection rate of prosthetic devices. Therefore, to achieve high accuracy and arm position stability in upper-arm motion recognition, we propose a Deep Adversarial Inception Domain Adaptation (DAIDA) based on the Inception feature module to enhance the generalization ability of the model. Surface electromyography (sEMG) signals were collected from 10 healthy subjects and two transhumeral amputees while performing hand, wrist, and elbow motions at three arm positions. The recognition performance of different feature modules was compared, and ultimately, accurate recognition of upper-arm motions was achieved using the Inception C module with a recognition accuracy of 90.70% ± 9.27%. Subsequently, validation was performed using data from different arm positions as source and target domains, and the results showed that compared to the direct use of a convolutional neural network (CNN), the recognition accuracy on untrained arm positions increased by 75.71% (p &lt; 0.05), with a recognition accuracy of 91.25% ± 6.59%. Similarly, in testing scenarios involving multiple arm positions, there was a significant improvement in recognition accuracy, with recognition accuracy exceeding 90% for both healthy subjects and transhumeral amputees.

Comparison of Metabolic Power and Energy Cost of Submaximal and Sprint Running Efforts Using Different Methods in Elite Youth Soccer Players: A Novel Energetic Approach.

Sprinting is a decisive action in soccer that is considerably taxing from a neuromuscular and energetic perspective. This study compared different calculation methods for the metabolic power (MP) and energy cost (EC) of sprinting using global positioning system (GPS) metrics and electromyography (EMG), with the aim of identifying potential differences in performance markers. Sixteen elite U17 male soccer players (age: 16.4 ± 0.5 years; body mass: 64.6 ± 4.4 kg; and height: 177.4 ± 4.3 cm) participated in the study and completed four different submaximal constant running efforts followed by sprinting actions while using portable GPS-IMU units and surface EMG. GPS-derived MP was determined based on GPS velocity, and the EMG-MP and EC were calculated based on individual profiles plotting the MP of the GPS and all EMG signals acquired. The goodness of fit of the linear regressions was assessed by the coefficient of determination (R2), and a repeated measures ANOVA was used to detect changes. A linear trend was found in EMG activity during submaximal speed runs (R2 = 1), but when the sprint effort was considered, the trend became exponential (R2 = 0.89). The EMG/force ratio displayed two different trends: linear up to a 30 m sprint (R2 = 0.99) and polynomial up to a 50 m sprint (R2 = 0.96). Statistically significant differences between the GPS and EMG were observed for MP splits at 0-5 m, 5-10 m, 25-30 m, 30-35 m, and 35-40 m and for EC splits at 5-10 m, 25-30 m, 30-35 m, and 35-40 m (p ≤ 0.05). Therefore, the determination of the MP and EC based on GPS technology underestimated the neuromuscular and metabolic engagement during the sprinting efforts. Thus, the EMG-derived method seems to be more accurate for calculating the MP and EC in this type of action.

Evaluation of physiological response and synchronisation errors during synchronous and pseudosynchronous stimulation trials

Rhythm perception and synchronisation is musical ability with neural basis defined as the ability to perceive rhythm in music and synchronise body movements with it. The study aimed to check the errors of synchronisation and physiological response as a reaction of the subjects to metrorhythmic stimuli of synchronous and pseudosynchronous stimulation (synchronisation with an externally controlled rhythm, but in reality controlled or produced tone by tapping) Nineteen subjects without diagnosed motor disorders participated in the study. Two tests were performed, where the electromyography signal and reaction time were recorded using the NORAXON system. In addition, physiological signals such as electrodermal activity and blood volume pulse were measured using the Empatica E4. Study 1 consisted of adapting the finger tapping test in pseudosynchrony with a given metrorhythmic stimulus with a selection of preferred, choices of decreasing and increasing tempo. Study 2 consisted of metrorhythmic synchronisation during the heel stomping test. Numerous correlations and statistically significant parameters were found between the response of the subjects with respect to their musical education, musical and sports activities. Most of the differentiating characteristics shown evidence of some group division in the undertaking of musical activities. The use of detailed analyses of synchronisation errors can contribute to the development of methods to improve the rehabilitation process of subjects with motor dysfunction, and this will contribute to the development of an expert system that considers personalised musical preferences.

Wearable Sensor‐Based Multi‐modal Fusion Network for Automated Gait Dysfunction Assessment in Children with Cerebral Palsy

Gait, fundamental to human movement, becomes compromised in cerebral palsy (CP), a childhood‐onset central nervous system motor disorder. Precise assessment of patients’ gait is crucial for tailored rehabilitation interventions. Currently, clinical scales assessing CP gait dysfunction mostly, while valuable, rely on subjective clinician observations. To enhance objectivity and efficiency in CP diagnosis and rehabilitation, there is a need for more objective assessment procedures. This study introduces a multi‐modal and multi‐scale feature fusion (MMFF) framework, a new framework for automating gait dysfunction assessment in children with CP. By utilizing surface electromyography and acceleration signals recorded during children's walking, MMFF generates a feature vector enriched with adaptively refined feature maps, cross‐mode correlations, and both local and global information. Validation of MMFF's effectiveness is evident through an accomplished classification accuracy of 99.13%. The mean values for precision, recall, and F1‐score in Gross Motor Function Classification System (GMFCS)‐1, GMFCS‐2, and GMFCS‐3, reaching 99.00%, 99.00%, and 98.33%, respectively, further reflect the accuracy of functional assessments at each level. This study underscores MMFF's potential as an objective, streamlined tool for clinicians, promising improved gait assessment and well‐informed rehabilitation strategies for children with CP.

FFCSLT: A Deep Learning Model for Traffic Police Hand Gesture Recognition Using Surface Electromyographic Signals

FFCSLT: A Deep Learning Model for Traffic Police Hand Gesture Recognition Using Surface Electromyographic Signals

Automated detection and removal of artifacts from sEMG signals based on fuzzy inference system and signal decomposition methods

Surface electromyography (sEMG) signal quality decreases when it is contaminated by different types of artifacts. Detection and removal of the contaminants from sEMG signals were a major and interesting task for several research works in recent years. In this paper, a novel automatic approach was designed for the detection and removal of artifacts from sEMG signals. The proposed method consists of a combined model that based on a fuzzy inference system (FIS), a signal decomposition method, and a denoising technique. Based on three spectral statistical features, which are calculated in frequency domain using power spectral density: composite multiscale entropy (CMSE), kurtosis (K), and skewness (S), FIS detects the artifactual epochs of the sEMG signal. Thereafter, those epochs are decomposed into their components by a signal decomposition. In this work, we have tested the performance of two decomposition methods namely stationary wavelet transform (SWT) and variational mode decomposition (VMD). A second FIS is used to select the contaminated components. Depending on the type of artifact, two procedures are considered, the first aims to denoise motion artifact (MOA), electrocardiography interference (ECG) and power line interference (PLI), whereas the second procedure aims to eliminate the additive white gaussian noise (AWGN). Finally, the denoised signal is reconstructed. The evaluation performance of the proposed method is compared with the performance of some existing methods using the following metrics: SNR improvement (ΔSNR), the reduction in artifact (λ(%)), improvement in signal to noise and distortion ratio (ΔSNDR) and improvement in the root mean square error (ΔRMSE). The results emphazed the superiority of our method over others in terms of all the used metrics and computational time.