Electrophysiological method to examine muscle fiber architecture in the upper lip in cleft-lip patients

Radeke, Johanna; Dijk, Hans van; Holobar, Aleš; Lapatki, Bernd G.

doi:10.1007/s00056-013-0193-5

Cited by 13 publications

(6 citation statements)

References 24 publications

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“…It has been reported in previous studies that the orientation and position of the electrodes affects the reproducibility of EMG measurements. Thus, in this study, the positioning of the bipolar electrodes was in accordance with that described in previous studies to allow direct comparison ( 9 , 11 , 34 , 35 ). A recent study documents the optimal placement strategy for monopolar electrodes in the inferior orbicularis oris, but not for the superior oris ( 36 ).…”

Section: Discussionmentioning

confidence: 89%

Comparison of EMG activity and blood flow during graded exertion in the orbicularis oris muscle of adult subjects with and without lip incompetence: a cross-sectional survey

Takada

Miyamoto

Sato

et al. 2017

European Journal of Orthodontics

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SummaryBackground/ObjectivesThe peri-oral muscles—including orbicularis oris—are critical in maintaining equilibrium in tooth position. Lip incompetence (LI) can thus be a factor in malocclusion. We therefore aimed to validate a technique to evaluate not only muscle activity via electromyography (EMG) but also muscle endurance and fatigue via blood flow (BF) for LI.Subjects/MethodsSubjects were classified into increased muscle tension/lip incompetent (experimental) and normal muscle tension/lip competent (control) groups. Each subject then exerted force on a custom-made traction plate connected to a tension gauge. Using laser speckle imaging and electromyographic measurements, we characterized muscle activity and corresponding BF rates in these subjects in various states of resting, loading, and recovery.ResultsResults showed a significant difference between the experimental and control groups, notably in the rate of change in BF to the inferior orbicularis oris muscle under conditions of increasing load (graded exertion). Furthermore, the data suggested that the muscles in the control group undergo a more prolonged (and therefore presumably more complete) recovery than muscles in the experimental group. These factors of reduced BF and short recovery may combine to accelerate muscle fatigue and produce LI.LimitationsThe sample used here was controlled for malocclusion (including open bite) to eliminate this type of confounding effect.Conclusions/ImplicationsFrom these findings, we conclude that reduced BF and inadequate recovery in the orbicularis oris muscles may be more significant than EMG activity in the assessment of LI.

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Section: Discussionmentioning

confidence: 89%

Comparison of EMG activity and blood flow during graded exertion in the orbicularis oris muscle of adult subjects with and without lip incompetence: a cross-sectional survey

Takada

Miyamoto

Sato

et al. 2017

European Journal of Orthodontics

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“…stands for mathematical expectation. The method has been tested in many different experimental setups [9], [10], [16]- [21], [29], [31], [41], [45], [47] also in moderate dynamic contractions [8], [11], [12], [26], [40] and yielded up to 70 simultaneously active MUs. Furthermore, Pulse-to-Noise (PNR) metric has been proposed in [21] for assessment of accuracy of each individual MU identification.…”

Section: A Ckc-based Identification Of Mu Firing Pattern and Correspmentioning

confidence: 99%

Comparison of Convolutive Kernel Compensation and Non-Negative Matrix Factorization of Surface Electromyograms

Šavc

Glaser

Kranjec

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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We compared non-negative matrix factorization (NMF) and convolution kernel compensation techniques for high-density electromyogram decomposition. The experimental data were recorded from nine healthy persons during controlled single degree of freedom (DOF) wrist flexion-extension, supination-pronation, and ulnar-radial deviation movements. We assembled the identified motor units and NMF components into three groups. Those active mostly during the first and the second movement direction per DOF were placed in the G1 and G3 groups, respectively. The remaining components were nonspecific for movement direction and were placed in the G2 group. In ulnar and radial deviation, the relative energies of identified cumulative motor unit spike trains (CSTs) and NMF components were similarly distributed among the groups. In other two movement types, the energy of NMF components in the G2 group was significantly larger than the energy of CSTs. We further performed a coherence analysis between CSTs and sums of NMF components in each group. Both decompositions demonstrated a solid match, but only at frequencies <3 Hz. At higher frequencies, the coherence hardly exceeded the value of 0.5. Potential reasons for these discrepancies include the negative impact of motor unit action potential shapes and noise on NMF decomposition.

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“…The performance of our algorithm was compared with that of the previously published gCKC algorithm (Table 3) since gCKC was shown to be accurate with different muscle architectures (Holobar et al, 2010; Marateb et al, 2011a; Webster et al, 2016) and different pathological conditions including essential tremor (Gallego et al, 2015a), Parkinson's disease (Holobar et al, 2012), diabetes (Watanabe et al, 2013), after stroke (Li et al, 2015), targeted muscle reinnervation (Farina et al, 2014c), and cleft lip surgery (Radeke et al, 2014). The previously introduced PNR, an efficient objective signal-based metric of gCKC decomposition accuracy (Holobar et al, 2014), was also reported for our decomposition results (Table 2, Figure 2).…”

Section: Discussionmentioning

confidence: 99%

Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

Mohebian

Marateb

Karimimehr

et al. 2019

Front. Comput. Neurosci.

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Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analysis of neural codes by decomposing the EMG, also known as neural decoding, is an alternative to EMG amplitude estimation. In this study, we propose a fully-deterministic hybrid surface EMG (sEMG) decomposition approach that combines the advantages of both template-based and Blind Source Separation (BSS) decomposition approaches, a.k.a. guided source separation (GSS), to identify motor unit (MU) firing patterns. We use the single-pass density-based clustering algorithm to identify possible cluster representatives in different sEMG channels. These cluster representatives are then used as initial points of modified gradient Convolution Kernel Compensation (gCKC) algorithm. Afterwards, we use the Kalman filter to reduce the noise impact and increase convergence rate of MU filter identification by gCKC. Moreover, we designed an adaptive soft-thresholding method to identify MU firing times out of estimated MU spike trains. We tested the proposed algorithm on a set of synthetic sEMG signals with known MU firing patterns. A grid of 9 × 10 monopolar surface electrodes with 5-mm inter-electrode distances in both directions was simulated. Muscle excitation was set to 10, 30, and 50%. Colored Gaussian zero-mean noise with the signal-to-noise ratio (SNR) of 10, 20, and 30 dB, respectively, was added to 16 s long sEMG signals that were sampled at 4,096 Hz. Overall, 45 simulated signals were analyzed. Our decomposition approach was compared with gCKC algorithm. Overall, in our algorithm, the average numbers of identified MUs and Rate-of-Agreement (RoA) were 16.41 ± 4.18 MUs and 84.00 ± 0.06%, respectively, whereas the gCKC identified 12.10 ± 2.32 MUs with the average RoA of 90.78 ± 0.08%. Therefore, the proposed GSS method identified more MUs than the gCKC, with comparable performance. Its performance was dependent on the signal quality but not the signal complexity at different force levels. The proposed algorithm is a promising new offline tool in clinical neurophysiology.

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Electrophysiological method to examine muscle fiber architecture in the upper lip in cleft-lip patients

Cited by 13 publications

References 24 publications

Comparison of EMG activity and blood flow during graded exertion in the orbicularis oris muscle of adult subjects with and without lip incompetence: a cross-sectional survey

Comparison of EMG activity and blood flow during graded exertion in the orbicularis oris muscle of adult subjects with and without lip incompetence: a cross-sectional survey

Comparison of Convolutive Kernel Compensation and Non-Negative Matrix Factorization of Surface Electromyograms

Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

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