Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation

Wang, Haipeng; Bi, Zheng-Yang; Fan, Wenjie; Zhou, Yixin; Zhou, Yuxuan; Li, Fēi; Wang, Keping; Lü, Xiaoying; Wang, Zhigong

doi:10.1109/access.2021.3077644

Cited by 7 publications

(5 citation statements)

References 30 publications

(63 reference statements)

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“…These results appear to be consistent with the literature, since most studies have reported using either the db2 [73][74][75][76][77][78][79] or the db4 [67,[80][81][82][83][84]. In terms of optimal decomposition level, level 4 appears to be the most widely used [5,19,71,77,82,85,86], while the level 3 is also frequently chosen [25,72,[87][88][89][90].…”

Section: Denoising Methods After Wavelet Decompositionsupporting

confidence: 87%

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals : A Review

Boyer¹,

Bouyer²,

Roy³

et al. 2023

Preprint

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EMG analysis is becoming increasingly important in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

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Section: Denoising Methods After Wavelet Decompositionsupporting

confidence: 87%

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals : A Review

Boyer¹,

Bouyer²,

Roy³

et al. 2023

Preprint

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show abstract

“…This will lead to the collected sEMG signal including all kinds of artifact components. Specifically, the artifacts can be divided into biological factors and technical factors ( Wang HP. et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Lu,

Gong,

Xue

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method.Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination.Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

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“…These results appear to be consistent with the literature, since most studies have reported using either the db2 [ 73 , 74 , 75 , 76 , 77 , 78 , 79 ] or the db4 [ 67 , 80 , 81 , 82 , 83 , 84 ]. In terms of the optimal decomposition level, level 4 appears to be the most widely used [ 5 , 19 , 71 , 77 , 82 , 85 , 86 ], while the level 3 is also frequently chosen [ 25 , 72 , 87 , 88 , 89 , 90 ].…”

Section: Contaminant Reduction Methodsmentioning

confidence: 99%

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

Boyer

Bouyer

Roy

et al. 2023

Sensors

View full text Add to dashboard Cite

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application.

show abstract

Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation

Cited by 7 publications

References 30 publications

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals : A Review

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals : A Review

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

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