Ultrasound-guided motor unit scanning electromyography.

INTRODUCTION/AIMS: Measuring the spatial dimensions of a single motor unit remains a challenging problem, and current techniques, such as scanning electromyography (EMG), tend to underestimate the true dimensions. In this study we aimed to estimate more accurately the dimensions of a single motor unit by developing a clinically applicable scanning EMG protocol that utilizes ultrasound imaging to visualize and target a transect through the center of a single motor unit. METHODS: Single motor unit twitches in the tibialis anterior muscles of healthy volunteers were elicited via stimulation of the fibular nerve, visualized with ultrasound, and targeted with an intramuscular EMG electrode. The electrode was moved by hand in small steps through the motor unit territory. Ultrasound video output was synchronized to EMG capture, and the needle position was tracked at each step. RESULTS: Eight recordings from six participants were collected. The technique was quick and easy to perform (mean time, 6.1 minutes) with reasonable spatial resolution (mean step size, 1.85 mm), yielding motor unit territory sizes between 1.53 and 14.65 mm (mean, 7.15 mm). DISCUSSION: Ultrasound-guided motor unit scanning EMG is a quick and accurate method for obtaining a targeted motor unit transect. This combination of two readily available clinical tools provides insights into the dimensions and internal structure of the motor unit as a marker for neuromuscular conditions.

Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges.

Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked least-squares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension. We refer to this algorithm as the multi-masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. The results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal. Graphical abstract The raw scanning-EMG signal, which can be composed by several discharges of the MU, is processed by the MMLSS algorithm so as to eliminate the artifact interference. Firstly, artifacts are detected for each discharge from the raw signal, obtaining a multi-discharge validity mask that indicates the samples that have been corrupted by artifacts. Secondly, a least-squares smoothing procedure simultaneously operating in the spatial dimension and among the discharges is applied to the raw signal. This second step is performed using only the not contaminated samples according to the validity mask. The resulting MMLSS-processed scanning-EMG signal is clean of artifact interference.

Three-dimensional amplitude characteristics of masseter motor units and representativeness of extracted motor unit samples.

OBJECTIVE: This study aimed to characterize amplitude topographies for masseter motor units (MUs) three-dimensionally, and to assess whether high-density surface electromyography (HDsEMG) is able to detect MU samples that represent the masseter's entire MU pool. METHODS: Ten healthy adult volunteers participated in the study, which combined three EMG techniques. A HDsEMG grid covering the entire masseter, and intramuscular fine-wire electrodes were used to obtain two independent MU samples for comparison. The MUs' amplitude profiles in the dimension of muscle depth were determined using scanning EMG. All data were recorded simultaneously during a low, constant contraction level controlled by 3D force feedback. RESULTS: The median medio-lateral diameter of 4.4 mm (range: 1.2-7.9 mm) for MUs detected by HDsEMG did not differ significantly (Mann-Whitney-U test, p = 0.805) from that of 3.9 mm (0.6-8.6 mm) for MUs detected by fine-wire EMG. For individual subjects, the medio-lateral diameters of all HDsEMG-detected MUs spanned 70.5% (19.2-75.1%) of the masseter's thickness. CONCLUSIONS: HDsEMG is able to examine small and large MUs from a great masseter proportion in one single measurement. SIGNIFICANCE: Clinical application of HDsEMG might contribute to a better understanding of neuromuscular adaptations in patients with temporomandibular disorders (TMD) and could allow for monitoring treatment effects.

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

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 Abstract The 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.

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

OBJECTIVE: To study motor unit activity in the medio-lateral extension of the masseter using an adapted scanning EMG technique that allows studying the territories of multiple motor units (MUs) in one scan. METHODS: We studied the m. masseter of 10 healthy volunteers in whom two scans were performed. A monopolar scanning needle and two pairs of fine-wire electrodes were inserted into the belly of the muscle. The signals of the fine wire electrodes were decomposed into the contribution of single MUs and used as a trigger for the scanning needle. In this manner multiple MU territory scans were obtained simultaneously. RESULTS: We determined 161 MU territories. The maximum number of territories obtained in one scan was 15. The median territory size was 4.0mm. Larger and smaller MU territories were found throughout the muscle. CONCLUSIONS: The presented technique showed its feasibility in obtaining multiple MU territories in one scan. MUs were active throughout the depth of the muscle. SIGNIFICANCE: The distribution of electrical and anatomical size of MUs substantiates the heterogeneous distribution of MUs throughout the muscle volume. This distributed activity may be of functional significance for the stabilization of the muscle during force generation.