Variational mode decomposition for surface and intramuscular EMG signal denoising

Ashraf, Hassan; Shafiq, U.; Sajjad, Q.; Waris, Asim; Gilani, O.; Boutaayamou, M.; Brüls, O.

doi:10.1016/j.bspc.2022.104560

Cited by 20 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first model centers on the reconstruction of a real EMG signal through the generation of a synthetic signal based on firing trains. This model does not contemplate the signal reconstruction in terms of amplitude or the relationship between the SMUAP waveform and signal recruitment [24], [25], [26].…”

Section: Signal Firing Train Modelmentioning

confidence: 99%

Design of an EMG Signal Generator Based on Random Firing Patterns

León,

López,

López

et al. 2024

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Electromyographic (EMG) signals exhibit complex interference patterns that comprise several single motor unit action potentials (SMUAPs). Evidence of a model that can generate EMG signals and considers intrinsic characteristics, such as long-range dependence (LRD) or shortrange dependence (SRD), or that supports the study of pathology-related signals is lacking. Therefore, the present study aimed to develop an EMG signal generator based on SRD or LRD derived from firing patterns. We used a dynamic model to parameterize up to 15 SMUAP waveforms of real EMG signals extracted from a database. Then, we used relative appearance rates for some signals based on the number of SMUAPs to generate the latter randomly. Furthermore, we complemented our model by generating a random firing pattern. The synthetic reconstruction of the signals indicated a displacement compared with their respective firing patterns, with the highest error rate being 4.1%. The model of the EMG signal generator in its current state could be useful for a specialist who intends to study the behavior of the signals, starting with the exploration of synthetic signals and then proceeding to the real signals.

show abstract

Section: Signal Firing Train Modelmentioning

confidence: 99%

Design of an EMG Signal Generator Based on Random Firing Patterns

León,

López,

López

et al. 2024

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

“…With the wide application of artificial intelligence in various fields, including long shortterm memory (LSTM) neural network and support vector machine (SVM) [14][15][16]. Ashraf et al (2023) [17] adopted a new denoising algorithm based on variational mode decomposition, and used SOFT iterative interval threshold to process surface and intramuscular electromyography. It was found that the combination of the IIT threshold technique and the SOFT threshold operator based on VMD produced the best denoising results while retaining the original signal characteristics.…”

Section: Plos Onementioning

confidence: 99%

Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis

Wang

2023

PLoS ONE

View full text Add to dashboard Cite

Objective This work was aimed to analyze the effect of vitamin energy drink on muscle fatigue by surface electromyography (SEMG) and ultrasonic bioimaging (USBI). Methods 20 healthy men were selected to do increasing load fatigue test. Surface electromyographic signals and ultrasonic biological images were collected based on wavelet threshold function with improved thresholds. Time domain and frequency domain characteristic integrated electromyography (IEMG), root mean square amplitude (RMS), average power frequency (MPF), and surface and deep muscle morphological changes were analyzed. Hemoglobin concentration (HB), red blood cell number (RBC), mean volume of red blood cell (MCV), blood lactic acid (BLA), malondialdehyde (MDA), and phosphocreatine kinase (CK) were measured. Results 1) the Accuracy (94.10%), Sensitivity (94.43%), Specificity (93.75%), and Precision (94.07%) of the long and short-term memory (LSTM) specificity for muscle fatigue recognition were higher than those of other models. 2) Compared with the control group, the levels of BLA, MDA, and CK in the experimental group were decreased and HB levels were increased after exercise (P < 0.05). 3) IEMG and RMS of the experimental group were higher than those of the control group, and increased with time (P < 0.05). 4) The mean amplitude of the response signal decreased with time. Compared with the control group, the surface muscle thickness, deep muscle thickness, total muscle thickness, contrast, and homogeneity (HOM) decreased in the experimental group; while the angular second moment (ASM) and contrast increased, showing great differences (P < 0.05). Conclusion Surface electromyographic signal and ultrasonic biological image can be used as auxiliary monitoring techniques for muscle fatigue during exercise. Drinking vitamin energy drinks before exercise can relieve physical fatigue to a certain extent and promote the maintenance of muscle microstructure.

show abstract

“…The artifact-contaminated segments identified are subsequently decomposed into multiple Variational Mode Functions (VMFs) using a genetic algorithm (GA)-optimized variational mode decomposition (GA-VMD) algorithm. Introduced by Dragomiretskiy et al [ 27 ] in 2014, VMD possesses adaptive characteristics, avoiding the limitations found in EMD and presenting superior noise robustness [ 28 , 29 ]. A previous study [ 30 ] demonstrated VMD’s efficacy in removing baseline drifts in pulse wave signals, effectively minimizing distortion.…”

Section: Introductionmentioning

confidence: 99%

Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification

Xiong,

Sun,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients.

show abstract

Variational mode decomposition for surface and intramuscular EMG signal denoising

Cited by 20 publications

References 39 publications

Design of an EMG Signal Generator Based on Random Firing Patterns

Design of an EMG Signal Generator Based on Random Firing Patterns

Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis

Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification

Contact Info

Product

Resources

About