emg patterns
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Author(s):  
Jade Yeung ◽  
Peter George Redmayne Burke ◽  
Fiona L. Knapman ◽  
Jessica Patti ◽  
Elizabeth C. Brown ◽  
...  

Anatomical and imaging evidence suggests neural control of oblique and horizontal compartments of the genioglossus differs. However, neurophysiological evidence for differential control remains elusive. This study aimed to determine whether there are differences in neural drive to the oblique and horizontal regions of the genioglossus during swallowing and tongue protrusion. Adult participants (N=63; 48M) were recruited from a sleep clinic; 41 had Obstructive Sleep Apnoea (OSA: 34M, 8F). Electromyographic (EMG) was recorded at rest (awake, supine) using 4 intramuscular fine-wire electrodes inserted percutaneously into the anterior oblique, posterior oblique, anterior horizontal and posterior horizontal genioglossus. Epiglottic pressure and nasal airflow were also measured. During swallowing, two distinct EMG patterns were observed- a monophasic response (single EMG peak) and a biphasic response (two bursts of EMG). Peak EMG and timing of the peak relative to epiglottic pressure were significantly different between patterns (linear mixed models, p<0.001). Monophasic activation was more likely in the horizontal than oblique region during swallowing (OR=6.83, CI=3.46-13.53, p<0.001). In contrast, during tongue protrusion, activation patterns and EMG magnitude were not different between regions. There were no systematic differences in EMG patterns during swallowing or tongue protrusion between OSA and non-OSA groups. These findings provide evidence for functional differences in the motoneuronal output to the oblique and horizontal compartments, enabling differential task-specific drive. Given this, it is important to identify the compartment from which EMG is acquired. We propose that the EMG patterns during swallowing may be used to identify the compartment where a recording electrode is located.


2021 ◽  
Vol 12 ◽  
Author(s):  
Shota Date ◽  
Hiroshi Kurumadani ◽  
Yuko Nakashima ◽  
Yosuke Ishii ◽  
Akio Ueda ◽  
...  

Muscle activities of the elbow flexors, especially the brachialis muscle (BR), have been measured with intramuscular electromyography (EMG) using the fine-wire electrodes. It remains unclear whether BR activity can be assessed using surface EMG. The purpose of this study was to compare the EMG patterns of the BR activity recorded during elbow flexion using surface and fine-wire electrodes and to determine whether surface EMG can accurately measure the BR activity. Six healthy men were asked to perform two tasks—a maximum isometric voluntary contractions (MVICs) task and an isotonic elbow-flexion task without lifting any weight. The surface and intramuscular EMG were simultaneously recorded from the BR and the long and short heads of the biceps brachii muscle (BBLH and BBSH, respectively). The locations of the muscles were identified and marked under ultrasonographic guidance. The peak cross-correlation coefficients between the EMG signals during the MVICs task were calculated. For the isotonic elbow-flexion task, the EMG patterns for activities of each muscle were compared between the surface and the fine-wire electrodes. All cross-correlation coefficients between the surface EMG signals from the muscles were lower than 0.3. Furthermore, the EMG patterns of the BR activity were not significantly different between the surface and the fine-wire electrodes. The BR has different EMG pattern from the BBLH and the BBSH. The BR activity, conventionally measured with intramuscular EMG, can be accurately accessed with surface EMG during elbow flexion performed without lifting any weight, independent from the BBLH and BBSH activities.


2021 ◽  
Vol 12 ◽  
Author(s):  
Christopher Fricke ◽  
Jalal Alizadeh ◽  
Nahrin Zakhary ◽  
Timo B. Woost ◽  
Martin Bogdan ◽  
...  

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation.


Author(s):  
Morten B. Kristoffersen ◽  
Andreas W. Franzke ◽  
Raoul M. Bongers ◽  
Michael Wand ◽  
Alessio Murgia ◽  
...  

Abstract Background Upper limb prosthetics with multiple degrees of freedom (DoFs) are still mostly operated through the clinical standard Direct Control scheme. Machine learning control, on the other hand, allows controlling multiple DoFs although it requires separable and consistent electromyogram (EMG) patterns. Whereas user training can improve EMG pattern quality, conventional training methods might limit user potential. Training with serious games might lead to higher quality EMG patterns and better functional outcomes. In this explorative study we compare outcomes of serious game training with conventional training, and machine learning control with the users’ own one DoF prosthesis. Methods Participants with upper limb absence participated in 7 training sessions where they learned to control a 3 DoF prosthesis with two grips which was fitted. Participants received either game training or conventional training. Conventional training was based on coaching, as described in the literature. Game-based training was conducted using two games that trained EMG pattern separability and functional use. Both groups also trained functional use with the prosthesis donned. The prosthesis system was controlled using a neural network regressor. Outcome measures were EMG metrics, number of DoFs used, the spherical subset of the Southampton Hand Assessment Procedure and the Clothespin Relocation Test. Results Eight participants were recruited and four completed the study. Training did not lead to consistent improvements in EMG pattern quality or functional use, but some participants improved in some metrics. No differences were observed between the groups. Participants achieved consistently better results using their own prosthesis than the machine-learning controlled prosthesis used in this study. Conclusion Our explorative study showed in a small group of participants that serious game training seems to achieve similar results as conventional training. No consistent improvements were found in either group in terms of EMG metrics or functional use, which might be due to insufficient training. This study highlights the need for more research in user training for machine learning controlled prosthetics. In addition, this study contributes with more data comparing machine learning controlled prosthetics with Direct Controlled prosthetics.


2020 ◽  
Vol 62 ◽  
pp. 102140
Author(s):  
Morten B. Kristoffersen ◽  
Andreas W. Franzke ◽  
Corry K. van der Sluis ◽  
Alessio Murgia ◽  
Raoul M. Bongers

2020 ◽  
Vol 81 ◽  
pp. 97-98
Author(s):  
E. Flux ◽  
O.A. Atteveld ◽  
L. Bar-On ◽  
A.I. Buizer ◽  
J. Harlaar ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1551
Author(s):  
Israel Casado-Hernández ◽  
Ricardo Becerro-de-Bengoa-Vallejo ◽  
Marta Elena Losa-Iglesias ◽  
Eva María Martínez-Jiménez ◽  
Daniel López-López ◽  
...  

Nowadays, the use of insoles in sport practice have been recognized to decrease the foot and lower limb injury patterns. The aim of this study was to analyse the effect of four types of hardness insoles (HI) in the activity patterns of the hip and thigh muscles (HTM) in motoriders during motorcycling sport. The study was a crossover trial. Subjects were elite motoriders. The mean age was 33 ± 5.14 years. Electromyography (EMG) of hip and thigh muscles (HTM) data was registered via surface while subjects were riding on an elite motorcycle simulator. Subjects had to complete different tests with randomly hardest insoles (HI): 1: only polypropylene (58° D Shore); 2: Polypropylene (58° D Shore) with selective aluminium in hallux and metatarsal heads (60 HB Brinell hardness); 3: Ethylene vinyl acetate (EVA) (52° A Shore); and finally, 4: Ordinary EVA (25° A Shore) as the control. EMG patterns of the HTM, riding on an elite motorcycle simulator, showed the lowest peak amplitude with the insoles with polypropylene and selective aluminium. Using the hardest insoles in our study (selective aluminium) the EMG amplitude peaks decreased in all HTM.


Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 500 ◽  
Author(s):  
Sergey A. Lobov ◽  
Andrey V. Chernyshov ◽  
Nadia P. Krilova ◽  
Maxim O. Shamshin ◽  
Victor B. Kazantsev

One of the modern trends in the design of human–machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the “winner takes all” principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.


2018 ◽  
Vol 50 (6) ◽  
pp. 466-474 ◽  
Author(s):  
I. V. Vereshchaka ◽  
A. V. Gorkovenko ◽  
O. V. Lehedza ◽  
T. I. Abramovych ◽  
W. Pilewska ◽  
...  

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