Électromyographie

Résumé

L'électromyographie (EMG) est une technique médicale qui permet d'étudier la fonction des nerfs et des muscles = le système nerveux périphérique, les muscles et la jonction neuromusculaire, et donc de compléter certains diagnostics neurologiques. L’Electro Neuro Myo Graphie (ENMG) se divise en deux examens paracliniques distincts : la neurographie motrice (EMG) et la neurographie sensitive. Étymologie Du grec ancien / élektrôn, / muos, « muscle » et / graphein, « écrire ». Principe général Pour cela, on étudie les potentiels émis par le muscle lors de sa contraction volontaire. L'électrodiagnostic est une méthode de diagnostic utilisant des courants électriques. Il donne un accès à tous les troncs nerveux et tous les muscles assez superficiels. Le protocole d'examen inclut le choix des différents territoires à explorer et des différentes techniques à appliquer. Il dépend du tableau clinique. Il s'agit de l'ensemble des états pathologiques qui caractér

Source officielle

https://fr.wikipedia.org/wiki/%C3%89lectromyographie

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Séances de cours associées (22)

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Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Parviz Ghaderi

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of density, and the trimmed mean absolute value of derivative density were computed for each sEMG channel. These features were tested for the classification of single tasks as well as of two tasks concurrently performed. For single tasks, correlation-based feature selection was used, and the features were then classified using linear discriminant analysis (LDA), non-linear support vector machines, and multi-layer perceptron. The eXtreme gradient boosting (XGBoost) classifier was used for the classification of two movements simultaneously performed. The second and third versions of the Ninapro dataset (conventional control) and Ameri's movement dataset (simultaneous control) were used to test the proposed features. For the Ninapro dataset, the overall accuracy of LDA using the TMD feature was 98.99 +/- 1.36% and 92.25 +/- 9.48% for able-bodied and amputee subjects, respectively. Using ensemble learning of the three classifiers, the average macro and micro-F-score, macro recall, and precision on the validation sets were 98.23 +/- 2.02, 98.32 +/- 1.93, 98.32 +/- 1.93, and 98.88 +/- 1.31%, respectively, for the intact subjects. The movement misclassification percentage was 1.75 +/- 1.73 and 3.44 +/- 2.23 for the intact subjects and amputees. The proposed features were significantly correlated with the movement classes [Generalized Linear Model (GLM); P-value < 0.05]. An accurate online implementation of the proposed algorithm was also presented. For the simultaneous control, the overall accuracy was 99.71 +/- 0.08 and 97.85 +/- 0.10 for the XGBoost and LDA classifiers, respectively. The proposed features are thus promising for conventional and simultaneous myoelectric control.

FRONTIERS MEDIA SA2022

Chargement

A Adaptive Algorithm for Online Interference Cancellation in EMG Sensors

Hakan Gökcesu, Kaan Gökcesu

One of the biggest issues encountered in the analysis of sensitive electromyography (EMG) sensor data is the power line interference (PLI). Conventional methods in literature either lose valuable sensor data or inadequately decrease the power line noise. Instead of filtering out predetermined frequencies, adaptively estimating the spectrum of PM can provide better performance. This paper introduces an online adaptive algorithm that removes the power line interference in real time without disturbing the true EMG data. Our method sequentially processes the biomedical signal to properly estimate and remove the PLI component. Through experiments with real EMG data, we compared our method to the five state-of-the-art techniques. Our algorithm outperformed all of them with the highest SNR gain (3.6 dB on average) and with the least disturbance of the true EMG signal (0.0152 dB loss on average). Our method reduces the PLI the most while keeping the valuable sensor data loss at its minimum in comparison to the state-of-the-art. Reducing the noise without disturbing the valuable sensor data provides higher quality signals with decreased interference, which can be better processed and used in biomedical research.

2019

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Simultaneous prediction of wrist and hand motions is essential for the natural interaction with hand prostheses. In this paper, we propose a novel multi-out Gaussian process (MOGP) model and a multi-task deep learning (MTDL) algorithm to achieve simultaneous prediction of wrist rotation (pronation/supination) and finger gestures for transradial amputees via a wearable ultrasound array. We target six finger gestures with concurrent wrist rotation in four transradial amputees. Results show that MOGP outperforms previously reported subclass discriminant analysis for both predictions of discrete finger gestures and continuous wrist rotation. Moreover, we find that MTDL has the potential to improve the accuracy of finger gesture prediction compared to MOGP and classification-specific deep learning, albeit at the expense of reducing the accuracy of wrist rotation prediction. Extended comparative analysis shows the superiority of ultrasound over surface electromyography. This paper prioritizes exploring the performance of wearable ultrasound on the simultaneous prediction of wrist and hand motions for transradial amputees, demonstrating the potential of ultrasound in future prosthetic control. Our ultrasound-based adaptive prosthetic control dataset (Ultra-Pro) will be released to promote the development of the prosthetic community.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Chargement

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Parviz Ghaderi

FRONTIERS MEDIA SA2022