Enhancing classification accuracy of wrist movement by denoising sEMG signals

Abbas, Baqar; Farooq, Omar; Uzzaman, Yusuf; Khan, Abid Ali; Vyas, Aparna

doi:10.1109/embc.2013.6610860

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…They reported better results using the db4 at level 3. 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 Methodssupporting

confidence: 92%

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

Boyer

Bouyer

Roy

et al. 2023

Sensors

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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.

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Section: Contaminant Reduction Methodssupporting

confidence: 92%

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

show abstract

“…They reported better results using the db4 at level 3. 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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“…The proposed method uses a cubical function that yields more reduction in the transformed amplitude near zero and no reduction at threshold λ The values above λ are not altered. The cubical thresholding method can be defined mathematically by 60 :…”

Section: Methodsmentioning

confidence: 99%

A Wavelet-Statistical Features Approach for Nonconvulsive Seizure Detection

et al. 2014

Self Cite

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The detection of nonconvulsive seizures (NCSz) is a challenge because of the lack of physical symptoms, which may delay the diagnosis of the disease. Many researchers have reported automatic detection of seizures. However, few investigators have concentrated on detection of NCSz. This article proposes a method for reliable detection of NCSz. The electroencephalography (EEG) signal is usually contaminated by various nonstationary noises. Signal denoising is an important preprocessing step in the analysis of such signals. In this study, a new wavelet-based denoising approach using cubical thresholding has been proposed to reduce noise from the EEG signal prior to analysis. Three statistical features were extracted from wavelet frequency bands, encompassing the frequency range of 0 to 8, 8 to 16, 16 to 32, and 0 to 32 Hz. Extracted features were used to train linear classifier to discriminate between normal and seizure EEGs. The performance of the method was tested on a database of nine patients with 24 seizures in 80 hours of EEG recording. All the seizures were successfully detected, and false positive rate was found to be 0.7 per hour.

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Enhancing classification accuracy of wrist movement by denoising sEMG signals

Cited by 8 publications

References 11 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

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

A Wavelet-Statistical Features Approach for Nonconvulsive Seizure Detection

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