A simulation study on the relation between the motor unit depth and action potential from multi-channel surface electromyography recordings

He, Jingsong; Luo, Zirong

doi:10.1016/j.jocn.2018.05.005

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The stable sEMG signals of 10 seconds are first filtered by a bandpass filter (5-500 Hz) and a notched filter to eliminate the 60 Hz frequency. Then, the K-means convolution kernel compensation (KmCKC) algorithm was employed to decompose the composite sEMG signal into constituent pulse trains [13,19]. In brief, the K-means clustering method is used to classify the peaks detected from the global activity index into three groups.…”

Section: Hd-semg Signal Processingmentioning

confidence: 99%

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Alterations of Motor Unit Characteristics Associated With Muscle Fatigue

He,

Houston,

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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This study aims to characterize motor unit (MU) features associated with muscle fatigue, using high-density surface electromyography (HD-sEMG). The same MUs recruited before / after, and during muscle fatigue were identified for analysis. The surface location of the innervation zones (IZs) of the MUs was identified from the HD-sEMG bipolar motor unit action potential (MUAP) map. The depth of the MU was also identified from the decay pattern of the MUAP along the muscle fiber transverse direction. Both the surface IZ location and the MU depth information were utilized to ensure the same MU was examined during the contraction before / after muscle fatigue. The MUAP similarity, defined as the correlation coefficient between MUAP morphology, was adopted to reveal the alterations in MU characteristics under the condition of fatigue. The biomarkers of the same MUs were compared before / after fatigue (task 1) at 5%, 10%, and 15% maximal voluntary contraction (MVC) and in the process of continuous fatigue (task 2) at 20% MVC. Our results indicate that the MUAP morphology similarity of the same MUs was 0.91 ± 0.06 (task 1) and 0.93 ± 0.04 (task 2). The results showed that MUAP morphology maintained good stability before / after, and during muscle fatigue. The findings of this study may advance our understanding of the mechanism of MU neuromuscular fatigue.

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Section: Hd-semg Signal Processingmentioning

confidence: 99%

“…If all the firing times of IPT are known in advance, the estimated equation for X C  can be written as [19]:…”

Section: Hd-semg Signal Processingmentioning

confidence: 99%

Alterations of Motor Unit Characteristics Associated With Muscle Fatigue

He,

Houston,

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Inferring position of motor units from high-density surface EMG

Lundsberg,

Björkman,

Malesevic

et al. 2024

Sci Rep

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The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level.

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Electrodes Adaptive Model in Estimating the Depth of Motor Unit: A Motor Unit Action Potential Based Approach

Xia

Chen

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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A simulation study on the relation between the motor unit depth and action potential from multi-channel surface electromyography recordings

Cited by 4 publications

References 18 publications

Alterations of Motor Unit Characteristics Associated With Muscle Fatigue

Alterations of Motor Unit Characteristics Associated With Muscle Fatigue

Inferring position of motor units from high-density surface EMG

Electrodes Adaptive Model in Estimating the Depth of Motor Unit: A Motor Unit Action Potential Based Approach

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