Motor unit activity within the depth of the masseter characterized by an adapted scanning EMG technique

Dijk, Hans van; Eiglsperger, Ulrike; Hellmann, Daniel; Giannakopoulos, Nikolaos Nikitas; McGill, Kevin C.; Schindler, H. J.; Lapatki, Bernd G.

doi:10.1016/j.clinph.2016.05.186

Cited by 8 publications

(9 citation statements)

References 21 publications

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“…Analysis of scanning-EMG signals has proved useful not only in the study of neuromuscular pathologies [ 3 , 7 , 8 , 10 , 12 , 13 , 33 ] but also, and specially, in the detailed characterization of the anatomical properties of the MU [ 3 , 10 , 17 , 30 , 32 , 35 ]. Scanning-EMG signal analysis results in extraction from the signal of several useful descriptive parameters, such as the length of the MU territory [ 10 ], the number of MU fractions [ 31 ], the temporal delay between fractions [ 17 ], and the number of silent zones [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

Corera

Eciolaza

Rubio

et al. 2018

Med Biol Eng Comput

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Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components. Graphical AbstractThe raw scanning-EMG signal (left figure) is processed by the MLSS algorithm in order to remove the artifact interference. Firstly, artifacts are detected from the raw signal, obtaining a validity mask (central figure) that determines the samples that have been contaminated by artifacts. Secondly, a least-squares smoothing procedure in the spatial dimension is applied to the raw signal using the not contaminated samples according to the validity mask. The resulting MLSS-processed scanning-EMG signal (right figure) is clean of artifact interference.

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Section: Introductionmentioning

confidence: 99%

A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

Corera

Eciolaza

Rubio

et al. 2018

Med Biol Eng Comput

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“…As a next step, accounting for the contribution of the temporalis muscle would enhance force and deformation predictions by the finite element model [4]. Further improvements could be obtained through a systematic quantification of muscle recruitment patterns during mastication in dependence of biting location and bite force using electromyography [11,45]. Electromyography measurements could be useful to determine the changes in muscle activation patterns associated with eating and temporomandibular joint disorders [1,46,47].…”

Section: Resultsmentioning

confidence: 99%

Experimental and Numerical Characterization of the Mechanical Masseter Muscle Response During Biting

Weickenmeier

Jabareen

Révérend

et al. 2017

Journal of Biomechanical Engineering

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Predictive simulations of the mastication system would significantly improve our understanding of temporomandibular joint (TMJ) disorders and the planning of cranio-maxillofacial surgery procedures. Respective computational models must be validated by experimental data from in vivo characterization of the mastication system's mechanical response. The present pilot-study demonstrates the feasibility of a combined experimental and numerical procedure to validate a computer model of the masseter muscle. An experimental setup is proposed that provides a simultaneous bite force measurement and ultrasound-based visualization of muscle deformation. The direct comparison of the experimentally observed and numerically predicted muscle response demonstrates the predictive capabilities of such anatomically accurate biting models. Differences between molar and incisor biting are investigated; muscle deformation is recorded for three different bite forces in order to capture the effect of increasing muscle fiber recruitment. The three-dimensional (3D) muscle deformation at each bite position and force-level is approximatively reconstructed from ultrasound measurements in five distinct cross-sectional areas (four horizontal and one vertical cross section). The experimental work is accompanied by numerical simulations to validate the predictive capabilities of a constitutive muscle model previously formulated. An anatomy-based, fully 3D model of the masseter muscle is created from magnetic resonance images (MRI) of the same subject. The direct comparison of experimental and numerical results revealed good agreement for maximum bite forces and masseter deformations in both biting positions. The present work therefore presents a feasible in vivo measurement system to validate numerically predicted masseter muscle contractions during mastication.

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“…Multi-scanning-EMG is a modified implementation of the scanning-EMG technique that allows to simultaneously record the electrical activity of several MUs in a single scan [136,92,60], i.e., with a single insertion of the scanning electrode. This provides a great advantage over the conventional scanning-EMG technique, in which the recording procedure would have to be repeated as many times as the number of MUs wanted to be investigated, implying one insertion per MU.…”

Section: Multi-scanning-emgmentioning

confidence: 99%

“…Therefore, the trigger electrode must be suitable to record the activity of a wide region of the volume conductor. In [136], two pairs of intramuscular fine-wire electrodes were used as trigger electrode, while in [60], a two-dimensional grid of 256 surface electrodes placed on the skin was used for this purpose. A decomposition algorithm is used to extract the firing pattern of the MUs from the trigger signal.…”

Section: Multi-scanning-emgmentioning

confidence: 99%

“…When multiple MUP discharges per position are recorded, artifacts can be removed by averaging the different discharges existing at each position using mean or median [119]. If this discharge averaging is followed by a spatial median filtering [136,60], a much more effective artifact elimination can be achieved. This is the procedure typically used to eliminate noise in scanning-EMG signals.…”

Section: Spacementioning

confidence: 99%

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Estimation of the motor unit structure from scanning electromyography and surface electromyography recordings

Corera¹,

Rodriguez‐Falces²

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In this thesis, several algorithmic procedures to estimate the MU fiber parameters from the waveform of a scanning-EMG signal (i.e., from the recording of a MUP in multiple positions along a linear corridor) have been developed. The different procedures correspond to different modifications of the scanning-EMG technique, which differ in the number of scanning needles and recording ports used to record the signal. The three proposed variants are: the 1-port recording, based on a single scanning needle with a single recording port placed at one side of the needle (i.e., a single fiber EMG needle); the 2-port recording, based on a single scanning needle, with two recording ports, placed at opposite sides of the needle; and the 4-port recording, based on two scanning needles, each one with two recording ports. The estimation system also uses a linear array of surface-EMG recordings to obtain complementary information about the MU. In this way, the recording setup to achieve the MU parameter estimation consists on a simultaneous surface- and scanning-EMG signal recording of the MUP. The estimation system has been evaluated for the three scanning-EMG recording configurations (1-port, 2-port, and 4-port), and compared to the case in which the estimation is performed from a MUP recorded at a single position. The evaluation has been done in a simulation framework, using state of the art models of the muscle, MUs, recruitment, and needles, and developing specific models for the simultaneous surface- and scanning-EMG recording process. This provides a controlled environment in which the performance of the system can be objectively quantified and evaluated.The results evidence that MU parameters are estimated much more accurately when using the scanning-EMG technique than when using a MUP recorded at a single position, corroborating the hypothesis that the use of signals recorded at multiple positions enhances the parameter estimation. Among the three proposed recording configurations, the poorest estimation results have been obtained for the 1-port configuration which, moreover, is only capable of estimating the MU fibers at one side of the needle. The 2-port configuration gives better results, and allows to estimate the MU fibers at both sides of the needle. The 4-port configuration is the one that provides the best performance, but it has the disadvantage of being the most difficult configuration to be physically implemented. In the view of these results, a deeper evaluation of the 2-port recording configuration is done. This is because it combines a good estimation performance with a relative ease to be physically implemented. An additional effort is done to calculate several global MU parameters, such as the MU fiber density, the average potential propagation velocity of the fibers, and the width of the innervation zone, from the resulting set of estimated fibers. These global MU parameters provide relevant physiological information from a clinical point of view. Hence global parameters connect the estimation system developed in this thesis with a future application in the diagnosis and follow-up of neuromuscular pathologies.

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Motor unit activity within the depth of the masseter characterized by an adapted scanning EMG technique

Cited by 8 publications

References 21 publications

A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

Experimental and Numerical Characterization of the Mechanical Masseter Muscle Response During Biting

Estimation of the motor unit structure from scanning electromyography and surface electromyography recordings

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