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

Boyer, Marianne; Bouyer, Laurent J.; Roy, Jean-Sébastien; Campeau-Lecours, Alexandre

doi:10.3390/s23062927

Cited by 35 publications

(18 citation statements)

References 114 publications

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“…These include (1) low-frequency drift due to relatively slow conductance changes in the body of the informant, (2) electrical hum from the device's own power supply or from the general electromagnetic environment of the experimental setup, (3) broadband system noise of analog or digital origin, and (4) rogue static high frequencies in the 2-20 kHz range. This inventory of noise types is quite similar to that for other biophysical signals, such as the EMG [13]. When preparing for data collection with the EGG signal, it is therefore essential to first assess the signal quality by listening to the EGG signal and also to inspect it with full-range, narrow-band spectra and spectrograms.…”

Section: Types Of Noises In Egg Signalsmentioning

confidence: 93%

Pragmatic De-Noising of Electroglottographic Signals

Ternström

2024

Bioengineering

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In voice analysis, the electroglottographic (EGG) signal has long been recognized as a useful complement to the acoustic signal, but only when the vocal folds are actually contacting, such that this signal has an appreciable amplitude. However, phonation can also occur without the vocal folds contacting, as in breathy voice, in which case the EGG amplitude is low, but not zero. It is of great interest to identify the transition from non-contacting to contacting, because this will substantially change the nature of the vocal fold oscillations; however, that transition is not in itself audible. The magnitude of the cycle-normalized peak derivative of the EGG signal is a convenient indicator of vocal fold contacting, but no current EGG hardware has a sufficient signal-to-noise ratio of the derivative. We show how the textbook techniques of spectral thresholding and static notch filtering are straightforward to implement, can run in real time, and can mitigate several noise problems in EGG hardware. This can be useful to researchers in vocology.

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Section: Types Of Noises In Egg Signalsmentioning

confidence: 93%

Pragmatic De-Noising of Electroglottographic Signals

Ternström

2024

Bioengineering

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“…Therefore, a high pass filter was used to remove the artifacts effects [ 39 ]. The signal was filtered with a Notch Filter of 60 Hz to remove the DC power line interference, a common artifact observed in EMG signals due to electrical noise from the power supply [ 22 ]. This preprocessing step is crucial for ensuring the accuracy of EMG signal analysis by eliminating extraneous noise that can obscure the true physiological signals of interest.…”

Section: Methodsmentioning

confidence: 99%

“…This capability makes EMG a critical tool in a multidisciplinary approach to diagnosing and understanding KOA, alongside conventional imaging methods. However, the EMG signal is a complex signal affected by intrinsic and extrinsic noise due to motion artifacts, baseline noise, and interference noise [ 22 ]. This demands sophisticated processing analysis techniques to extract a significant insight into muscle-specific changes associated with KOA.…”

Section: Introductionmentioning

confidence: 99%

Combining enhanced spectral resolution of EMG and a deep learning approach for knee pathology diagnosis

Khader,

Zyout,

Al Fahoum

2024

PLoS ONE

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Knee osteoarthritis (OA) is a prevalent, debilitating joint condition primarily affecting the elderly. This investigation aims to develop an electromyography (EMG)-based method for diagnosing knee pathologies. EMG signals of the muscles surrounding the knee joint were examined and recorded. The principal components of the proposed method were preprocessing, high-order spectral analysis (HOSA), and diagnosis/recognition through deep learning. EMG signals from individuals with normal and OA knees while walking were extracted from a publicly available database. This examination focused on the quadriceps femoris, the medial gastrocnemius, the rectus femoris, the semitendinosus, and the vastus medialis. Filtration and rectification were utilized beforehand to eradicate noise and smooth EMG signals. Signals’ higher-order spectra were analyzed with HOSA to obtain information about nonlinear interactions and phase coupling. Initially, the bicoherence representation of EMG signals was devised. The resulting images were fed into a deep-learning system for identification and analysis. A deep learning algorithm using adapted ResNet101 CNN model examined the images to determine whether the EMG signals were conventional or indicative of knee osteoarthritis. The validated test results demonstrated high accuracy and robust metrics, indicating that the proposed method is effective. The medial gastrocnemius (MG) muscle was able to distinguish Knee osteoarthritis (KOA) patients from normal with 96.3±1.7% accuracy and 0.994±0.008 AUC. MG has the highest prediction accuracy of KOA and can be used as the muscle of interest in future analysis. Despite the proposed method’s superiority, some limitations still require special consideration and will be addressed in future research.

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“…Baseline noise refers to the electrical background noise of the device when it is not recording muscle activity [ 202 ]. Konrad defines the ideal baseline noise as 1–2 millivolts [ 1 ].…”

Section: Surface Electromyography In Dentistry—recommendations For Th...mentioning

confidence: 99%

Surface Electromyography in Dentistry—Past, Present and Future

Zieliński,

Gawda

2024

JCM

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Surface electromyography (sEMG) is a technique for measuring and analyzing the electrical signals of muscle activity using electrodes placed on the skin’s surface. The aim of this paper was to outline the history of the development and use of surface electromyography in dentistry, to show where research and technical solutions relating to surface electromyography currently lie, and to make recommendations for further research. sEMG is a diagnostic technique that has found significant application in dentistry. The historical section discusses the evolution of sEMG methods and equipment, highlighting how technological advances have influenced the accuracy and applicability of this method in dentistry. The need for standardization of musculoskeletal testing methodology is highlighted and the needed increased technical capabilities of sEMG equipment and the ability to specify parameters (e.g., sampling rates, bandwidth). A higher sampling rate (the recommended may be 2000 Hz or higher in masticatory muscles) allows more accurate recording of changes in the signal, which is essential for accurate analysis of muscle function. Bandwidth is one of the key parameters in sEMG research. Bandwidth determines the range of frequencies effectively recorded by the sEMG system (the recommended frequency limits are usually between 20 Hz and 500 Hz in masticatory muscles). In addition, the increased technical capabilities of sEMG equipment and the ability to specify electromyographic parameters demonstrate the need for a detailed description of selected parameters in the methodological section. This is necessary to maintain the reproducibility of sEMG testing. More high-quality clinical trials are needed in the future.

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

Cited by 35 publications

References 114 publications

Pragmatic De-Noising of Electroglottographic Signals

Pragmatic De-Noising of Electroglottographic Signals

Combining enhanced spectral resolution of EMG and a deep learning approach for knee pathology diagnosis

Surface Electromyography in Dentistry—Past, Present and Future

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