Standards of instrumentation of EMG Tankisi, Hatice; Burke, David; Cui, Liying ...
Clinical neurophysiology,
January 2020, 2020-Jan, 2020-01-00, 20200101, 2020, Letnik:
131, Številka:
1
Journal Article
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•Standard instrumentation ensures high quality recordings and enables comparison of results.•This consensus document on “Standards of Instrumentation of EMG” is written by an expert panel.•This ...report covers technical aspects as well as topics for optimal and standardized examinations.
Standardization of Electromyography (EMG) instrumentation is of particular importance to ensure high quality recordings. This consensus report on “Standards of Instrumentation of EMG” is an update and extension of the earlier IFCN Guidelines published in 1999. First, a panel of experts in different fields from different geographical distributions was invited to submit a section on their particular interest and expertise. Then, the merged document was circulated for comments and edits until a consensus emerged.
The first sections in this document cover technical aspects such as instrumentation, EMG hardware and software including amplifiers and filters, digital signal analysis and instrumentation settings. Other sections cover the topics such as temporary storage, trigger and delay line, averaging, electrode types, stimulation techniques for optimal and standardised EMG examinations, and the artefacts electromyographers may face and safety rules they should follow. Finally, storage of data and databases, report generators and external communication are summarized.
This tutorial intends to provide insight, instructions and “best practices” for those who are novices—including clinicians, engineers and non-engineers—in extracting electromyogram (EMG) amplitude ...from the bipolar surface EMG (sEMG) signal of voluntary contractions. A brief discussion of sEMG amplitude extraction from high density sEMG (HDsEMG) arrays and feature extraction from electrically elicited contractions is also provided.
This tutorial attempts to present its main concepts in a straightforward manner that is accessible to novices in the field not possessing a wide range of technical background (if any) in this area. Surface EMG amplitude, also referred to as the sEMG envelope often implemented as root mean square (RMS) sEMG or average rectified value (ARV) sEMG, quantifies the voltage variation of the sEMG signal and is grossly related to the overall neural excitation of the muscle and to peripheral parameters.
The tutorial briefly reviews the physiological origin of the voluntary sEMG signal and sEMG recording, including electrode configurations, sEMG signal transduction, electronic conditioning and conversion by an analog-to-digital converter. These topics have been covered in greater detail in prior tutorials in this series. In depth descriptions of state-of-the-art methods for computing sEMG amplitude are then provided, including guidance on signal pre-conditioning, absolute value vs. square-law detection, selection of appropriate sEMG amplitude smoothing filters and attenuation of measurement noise. The tutorial provides a detailed list of best practices for sEMG amplitude estimation.
When sampling electromyograms (EMGs) with one pair of electrodes, it seems implicitly assumed the detected signal reflects the net muscle excitation. However, this assumption is discredited by ...observations of local muscle excitation. Therefore, we hypothesize that the accurate assessment of muscle excitation requires multiple EMG detection and consideration of electrode-fiber alignment. We advise prudence when drawing inferences from individually collected EMGs.
Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance ...of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems.
Le CANVAS syndrome neurologique rare associe neuropathie sensitive, aréflexie vestibulaire et ataxie cérébelleuse. Il s’agit d’une pathologie autosomique récessive liée à l’expansion bi-allélique de ...l’intron AAAGG du gène RFC1.
À travers cette série de cas nous souhaitions établir les caractéristiques cliniques et électrophysiologiques de notre cohorte et de les confronter aux données de la littérature.
Nous avons recueilli les patients suivis au CHU d’Amiens pour lesquels le diagnostic de CANVAS avait été confirmé biologiquement. Nous avons recueilli les données neurophysiologiques : étude de la conduction nerveuse motrice et sensitive, réflexe H, blink reflex, étude en détection. Les données cliniques, les antécédents personnels et familiaux ont été consignés ainsi que les données paracliniques (imagerie, bilan ORL, bilan ophtalmologique, bilan paranéoplasique, bilan immunologique).
Huit patients (5 hommes, 3 femmes) présentaient un diagnostic confirmé de CANVAS. L’âge moyen de début des symptômes était de 53 ans, la symptomatologie évoluait en moyenne depuis 10,6 ans avec comme tableau initial soit des troubles de l’équilibre lentement évolutifs soit des troubles sensitifs des quatre membres. Cinq patients présentaient des antécédents de cancers familiaux ou personnels. Tous les patients présentaient une atteinte sensitive non longueur-dépendante, le réflexe H était préservé chez 7 patients.
Cette série illustre la présence des antécédents néoplasiques chez plus de la moitié des patients. Un lien potentiellement intéressant puisque la fonction des gènes RFC est d’activer l’ADN polymérase. La préservation du réflexe H chez la quasi-totalité de nos patients rejoint les données de la littérature, il s’agirait d’une neuropathie unique épargnant les fibres IA innervant les fibres musculaires afférentes.
Le CANVAS est une pathologie neurodégénérative multisystémique héréditaire dont la physiopathologie reste méconnue, notre série souligne la conservation du réflexe H et les antécédents néoplasiques.
A key challenge associated with myoelectric prosthesis limbs is the acquisition of a good quality gesture intent signal from the residual anatomy of an amputee. In this study, the authors aim to ...overcome this limitation by observing the classification accuracy of the fusion of wearable electromyography (EMG) and near‐infrared (NIR) to classify eight hand gesture motions across 12 able‐bodied participants. As part of the study, they investigate the classification accuracy across a multi‐layer perceptron neural network, linear discriminant analysis and quadratic discriminant analysis for different sensing configurations, i.e. EMG‐only, NIR‐only and EMG‐NIR. A separate offline ultrasound scan was conducted as part of the study and served as a ground truth and contrastive basis for the results picked up from the wearable sensors, and allowed for a closer study of the anatomy along the humerus during gesture motion. Results and findings from the work suggest that it could be possible to further develop transhumeral prosthesis using affordable, ergonomic and wearable EMG and NIR sensing, without the need for invasive neuromuscular sensors or further hardware complexity.
Les douleurs neuropathiques présentent une variation circadienne. Elles entraînent une altération de la qualité du sommeil. L’effet bénéfique du jeune intermittent est mis en évidence dans des études ...récentes.
Évaluer l’effet du jeune intermittent sur les douleurs neuropathiques. Étudier la corrélation de cet effet avec les données électromyographiques.
Étude cohorte colligeant 60 patients présentant une douleur neuropathique périphérique chronique confirmée par le score DN4. Ces patients ont suivi un jeune intermittent de 15 heures pendant 25 jours. Tous les patients ont eu un examen électroneuromyographique (ENMG). L’impact du jeune intermittent sur la douleur neuropathique a été évalué à l’aide de l’échelle numérique (EN), l’appréciation de la qualité du sommeil et l’évolution circadienne avant et durant le jeune.
Le jeune intermittent s’accompagne d’une modification de l’évolution circadienne de la douleur neuropathique chez 28,4 % des patients, d’une diminution de la moyenne du score EN (p<0,001). Une amélioration de la qualité de sommeil est rapportée (p<0,001). Cependant, aucune corrélation entre la sévérité électrique objectivée par électroneuromyogramme et l’effet du jeune sur les douleurs neuropathiques n’a été trouvé (p=0,1).
Des études cliniques sur le jeûne intermittent ont montré une amélioration des douleurs chroniques En effet, le jeune intermittent réduit le taux de cytokines pro-inflammatoires. Nos résultats confirment l’impact bénéfique du jeune intermittent. Cependant, la sévérité électrique n’a pas d’effet sur l’évolution de la douleur neuropathique durant le jeune intermittent.
Notre étude montre un effet bénéfique du jeune intermittent chez les patients souffrant de douleurs neuropathiques mais cet effet n’est pas corrélé à la sévérité électrique de la neuropathie.
Background
Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an ...effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown.
Methods
This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy.
Results
The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges.
Conclusions
Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.
Background
Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an ...effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown.
Methods
This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy.
Results
The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges.
Conclusions
Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.
•qEMG showed superior sensitivity to MScanFit MUNE in detecting abnormalities in amyotrophic lateral sclerosis (ALS) at diagnosis.•MScanFit MUNE parameters exhibited good correlations with both ...muscle strength and qEMG measures in muscles of ALS patients.•MScanFit MUNE and qEMG offered complementary insights for ALS diagnosis.
Motor Unit Number Estimation (MUNE) techniques are crucial in assessing lower motor neuron loss. MScanFit MUNE (MScanFit) is a novel tool which estimates MUNE values from compound muscle action potential (CMAP) scans by considering the probabilistic nature of motor unit firing. We conducted a prospective study to evaluate the diagnostic utility of MScanFit compared to quantitative electromyography (qEMG) in ALS patients.
We enrolled 35 patients diagnosed with amyotrophic lateral sclerosis (ALS) and 14 healthy controls, assessing qEMG and MScanFit MUNE in abductor pollicis brevis, abductor digiti minimi and tibialis anterior muscles.
We found higher sensitivity of qEMG in detecting abnormalities compared to MScanFit, with a high concordance rate between the two techniques. Notably, a few muscles exhibited abnormal MUNE but normal qEMG findings, suggesting a potential complementary role for MScanFit in ALS diagnosis. Neurophysiological parameters from MScanFit showed good correlations with qEMG measures. Subclinical neurophysiological involvement was observed in muscles with normal strength, emphasizing the importance of sensitive diagnostic tools.
MScanFit demonstrated validity in distinguishing ALS patients from healthy subjects and correlated well with qEMG parameters.
Our study confirmed the diagnostic utility of MScanFit MUNE in ALS, highlighting its role as a supplementary diagnostic tool.
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