scholarly journals Global Innervation Zone Identification With High-Density Surface Electromyography

2020 ◽  
Vol 67 (3) ◽  
pp. 718-725 ◽  
Author(s):  
Chuan Zhang ◽  
Nicholas Dias ◽  
Jinbao He ◽  
Ping Zhou ◽  
Sheng Li ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
High Density ◽  
Innervation Zone ◽  
Density Surface

scholarly journals Transcutaneous innervation zone imaging from high-density surface electromyography recordings

2020 ◽  
Vol 17 (1) ◽  
pp. 016070
Author(s):  
Yang Liu ◽  
Chuan Zhang ◽  
Nicholas Dias ◽  
Yen-Ting Chen ◽  
Sheng Li ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
High Density ◽  
Innervation Zone ◽  
Density Surface

Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities

2021 ◽  
Vol 95 ◽  
pp. 103456
Author(s):  
Tiwana Varrecchia ◽  
Alberto Ranavolo ◽  
Silvia Conforto ◽  
Alessandro Marco De Nunzio ◽  
Michail Arvanitidis ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
High Density ◽  
Frequency Dependent ◽  
Density Surface

scholarly journals Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Application in Neurorobotics

2021 ◽  
Author(s):  
Tianyun Sun ◽  
Qin Hu ◽  
Jacqueline Libby ◽  
S. Farokh Atashzar
Keyword(s):  
Surface Electromyography ◽  
Complex Model ◽  
High Density ◽  
Transient Phase ◽  
Spatiotemporal Resolution ◽  
Semg Signal ◽  
Training Time ◽  
Motor Intention ◽  
Density Surface ◽  
Human Robot Interfaces

Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, deepnets are generally challenged by long training times (affecting the practicality and calibration), complex model architectures (affecting the predictability of the outcomes), a large number of trainable parameters (increasing the need for big data), and possibly overfitting. Capitalizing on our recent work on homogeneous temporal dilation in a Recurrent Neural Network (RNN) model, this paper proposes, for the first time, heterogeneous temporal dilation in an LSTM model and applies that to high-density surface electromyography (HD-sEMG), allowing for decoding dynamic temporal dependencies with tunable temporal foci. In this paper, a 128-channel HD-sEMG signal space is considered due to the potential for enhancing the spatiotemporal resolution of human-robot interfaces. Accordingly, this paper addresses a challenging motor intention decoding problem of neurorobots, namely, transient intention identification. The aforementioned problem only takes into account the dynamic and transient phase of gesture movements when the signals are not stabilized or plateaued, addressing which can significantly enhance the temporal resolution of human-robot interfaces. This would eventually enhance seamless real-time implementations. Additionally, this paper introduces the concept of dilation foci to modulate the modeling of temporal variation in transient phases. In this work a high number (i.e. 65) of gestures is included, which adds to the complexity and significance of the understudied problem. Our results show state-of-the-art performance for gesture prediction in terms of accuracy, training time, and model convergence.

FC7.2 Using high-density surface electromyography (HD-sEMG) in detecting neuromuscular disorders in children

2006 ◽  
Vol 117 ◽  
pp. 1-2 ◽  
Author(s):  
J.P. van Dijk ◽  
D. Kusters ◽  
N. van Alfen ◽  
M.J. Zwarts ◽  
D.F. Stegeman ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
Neuromuscular Disorders ◽  
High Density ◽  
Density Surface

Identifying Motor Units in Longitudinal Studies with High-Density Surface Electromyography

2016 ◽  
pp. 147-151
Author(s):  
Eduardo Martinez-Valdes ◽  
Francesco Negro ◽  
Christopher M. Laine ◽  
Deborah L. Falla ◽  
Frank Mayer ◽  
...  
Keyword(s):  
Longitudinal Studies ◽  
Surface Electromyography ◽  
Motor Units ◽  
High Density ◽  
Density Surface

High‐density surface electromyography: A visualization method of laryngeal muscle activity

2019 ◽  
Vol 129 (10) ◽  
pp. 2347-2353 ◽  
Author(s):  
David J. Bracken ◽  
Gladys Ornelas ◽  
Todd P. Coleman ◽  
Philip A. Weissbrod
Keyword(s):  
Muscle Activity ◽  
Surface Electromyography ◽  
High Density ◽  
Visualization Method ◽  
Laryngeal Muscle ◽  
Density Surface

scholarly journals The rise and fall of fasciculations in amyotrophic lateral sclerosis

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
James A Bashford ◽  
Aidan Wickham ◽  
Raquel Iniesta ◽  
Emmanuel M Drakakis ◽  
Martyn G Boutelle ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Motor Unit ◽  
Surface Electromyography ◽  
Rating Scale ◽  
Muscle Power ◽  
Pooled Analysis ◽  
High Density ◽  
Mixed Effect ◽  
Density Surface ◽  
Lateral Sclerosis

Abstract Amyotrophic lateral sclerosis is a devastating neurodegenerative disease with a median survival of 3 years from symptom onset. Accessible and reliable biomarkers of motor neuron decline are urgently needed to quicken the pace of drug discovery. Fasciculations represent an early pathophysiological hallmark of amyotrophic lateral sclerosis and can be reliably detected by high-density surface electromyography. We set out to quantify fasciculation potentials prospectively over 14 months, seeking comparisons with established markers of disease progression. Twenty patients with amyotrophic lateral sclerosis and five patients with benign fasciculation syndrome underwent up to seven assessments each. At each assessment, we performed the amyotrophic lateral sclerosis-functional rating scale, sum power score, slow vital capacity, 30-min high-density surface electromyography recordings from biceps and gastrocnemius and the motor unit number index. We employed the Surface Potential Quantification Engine, which is an automated analytical tool to detect and characterize fasciculations. Linear mixed-effect models were employed to account for the pseudoreplication of serial measurements. The amyotrophic lateral sclerosis-functional rating scale declined by 0.65 points per month (P < 0.0001), 35% slower than average. A total of 526 recordings were analysed. Compared with benign fasciculation syndrome, biceps fasciculation frequency in amyotrophic lateral sclerosis was 10 times greater in strong muscles and 40 times greater in weak muscles. This was coupled with a decline in fasciculation frequency among weak muscles of –7.6/min per month (P = 0.003), demonstrating the rise and fall of fasciculation frequency in biceps muscles. Gastrocnemius behaved differently, whereby strong muscles in amyotrophic lateral sclerosis had fasciculation frequencies five times greater than patients with benign fasciculation syndrome while weak muscles were increased by only 1.5 times. Gastrocnemius demonstrated a significant decline in fasciculation frequency in strong muscles (−2.4/min per month, P < 0.0001), which levelled off in weak muscles. Fasciculation amplitude, an easily quantifiable surrogate of the reinnervation process, was highest in the biceps muscles that transitioned from strong to weak during the study. Pooled analysis of >900 000 fasciculations revealed inter-fasciculation intervals <100 ms in the biceps of patients with amyotrophic lateral sclerosis, particularly in strong muscles, consistent with the occurrence of doublets. We hereby present the most comprehensive longitudinal quantification of fasciculation parameters in amyotrophic lateral sclerosis, proposing a unifying model of the interactions between motor unit loss, muscle power and fasciculation frequency. The latter showed promise as a disease biomarker with linear rates of decline in strong gastrocnemius and weak biceps muscles, reflecting the motor unit loss that drives clinical progression.

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