Innervation zones of fasciculating motor units: observations by a linear electrode array

Neurophysiological and imaging biomarkers of lower motor neuron dysfunction in motor neuron diseases/amyotrophic lateral sclerosis: IFCN handbook chapter

This chapter discusses comprehensive neurophysiological biomarkers utilised in motor neuron disease (MND) and, in particular, its commonest form, amyotrophic lateral sclerosis (ALS). These encompass the conventional techniques including nerve conduction studies (NCS), needle and high-density surface electromyography (EMG) and H-reflex studies as well as novel techniques. In the last two decades, new methods of assessing the loss of motor units in a muscle have been developed, that are more convenient than earlier methods of motor unit number estimation (MUNE),and may use either electrical stimulation (e.g. MScanFit MUNE) or voluntary activation (MUNIX). Electrical impedance myography (EIM) is another novel approach for the evaluation that relies upon the application and measurement of high-frequency, low-intensity electrical current. Nerve excitability techniques (NET) also provide insights into the function of an axon and reflect the changes in resting membrane potential, ion channel dysfunction and the structural integrity of the axon and myelin sheath. Furthermore, imaging ultrasound techniques as well as magnetic resonance imaging are capable of detecting the constituents of morphological changes in the nerve and muscle. The chapter provides a critical description of the ability of each technique to provide neurophysiological insight into the complex pathophysiology of MND/ALS. However, it is important to recognise the strengths and limitations of each approach in order to clarify utility. These neurophysiological biomarkers have demonstrated reliability, specificity and provide additional information to validate and assess lower motor neuron dysfunction. Their use has expanded the knowledge about MND/ALS and enhanced our understanding of the relationship between motor units, axons, reflexes and other neural circuits in relation to clinical features of patients with MND/ALS at different stages of the disease. Taken together, the ultimate goal is to aid early diagnosis, distinguish potential disease mimics, monitor and stage disease progression, quantify response to treatment and develop potential therapeutic interventions.

Wearable bioelectronics fabricated in situ on skins

In recent years, wearable bioelectronics has rapidly expanded for diagnosing, monitoring, and treating various pathological conditions from the skin surface. Although the devices are typically prefabricated as soft patches for general usage, there is a growing need for devices that are customized in situ to provide accurate data and precise treatment. In this perspective, the state-of-the-art in situ fabricated wearable bioelectronics are summarized, focusing primarily on Drawn-on-Skin (DoS) bioelectronics and other in situ fabrication methods. The advantages and limitations of these technologies are evaluated and potential future directions are suggested for the widespread adoption of these technologies in everyday life.

Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform

This study examined methods for estimating the innervation zone (IZ) of a muscle using recorded monopolar high density M waves. Two IZ estimation methods based on principal component analysis (PCA) and Radon transform (RT) were examined. Experimental M waves, acquired from the biceps brachii muscles of nine healthy subjects were used as testing data sets. The performance of the two methods was evaluated by comparing their IZ estimations with manual IZ detection by experienced human operators. Compared with manual detection, the agreement rate of the estimated IZs was 83% and 63% for PCA and RT based methods, respectively, both using monopolar high density M waves. In contrast, the agreement rate was 56% for cross correlation analysis using bipolar high density M waves. The mean difference in estimated IZ location between manual detection and the tested method was 0.12 ± 0.28 inter-electrode-distance (IED) for PCA, 0.33 ± 0.41 IED for RT and 0.39 ± 0.74 IED for cross correlation-based methods. The results indicate that the PCA based method was able to automatically detect muscle IZs from monopolar M waves. Thus, PCA provides an alternative approach to estimate IZ location of voluntary or electrically-evoked muscle contractions, and may have particular value for IZ detection in patients with impaired voluntary muscle activation.

Customizable, reconfigurable, and anatomically coordinated large-area, high-density electromyography from drawn-on-skin electrode arrays

Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.

Teaching Essential EMG Theory to Kinesiologists and Physical Therapists Using Analogies Visual Descriptions, and Qualitative Analysis of Biophysical Concepts

Electromyography (EMG) is a multidisciplinary field that brings together allied health (kinesiology and physical therapy) and the engineering sciences (biomedical and electrical). Since the physical sciences are used in the measurement of a biological process, the presentation of the theoretical foundations of EMG is most conveniently conducted using math and physics. However, given the multidisciplinary nature of EMG, a course will most likely include students from diverse backgrounds, with varying levels of math and physics. This is a pedagogical paper that outlines an approach for teaching foundational concepts in EMG to kinesiologists and physical therapists that uses a combination of analogies, visual descriptions, and qualitative analysis of biophysical concepts to develop an intuitive understanding for those who are new to surface EMG. The approach focuses on muscle fiber action potentials (MFAPs), motor unit action potentials (MUAPs), and compound muscle action potentials (CMAPs) because changes in these waveforms are much easier to identify and describe in comparison to the surface EMG interference pattern (IP).

Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study

Surface electromyography (EMG) recorded by a linear or 2-dimensional electrode array can be used to estimate the location of muscle innervation zones (IZ). There are various neurophysiological factors that may influence surface EMG and thus potentially compromise muscle IZ estimation. The objective of this study was to evaluate how surface-EMG-based IZ estimation might be affected by different factors, including varying degrees of motor unit (MU) synchronization in the case of single or double IZs. The study was performed by implementing a model simulating surface EMG activity. Three different MU synchronization conditions were simulated, namely no synchronization, medium level synchronization, and complete synchronization analog to M wave. Surface EMG signals recorded by a 2-dimensional electrode array were simulated from a muscle with single and double IZs, respectively. For each situation, the IZ was estimated from surface EMG and compared with the one used in the model for performance evaluation. For the muscle with only one IZ, the estimated IZ location from surface EMG was consistent with the one used in the model for all the three MU synchronization conditions. For the muscle with double IZs, at least one IZ was appropriately estimated from interference surface EMG when there was no MU synchronization. However, the estimated IZ was different from either of the two IZ locations used in the model for the other two MU synchronization conditions. For muscles with a single IZ, MU synchronization has little effect on IZ estimation from electrode array surface EMG. However, caution is required for multiple IZ muscles since MU synchronization might lead to false IZ estimation.

The evolving role of surface electromyography in amyotrophic lateral sclerosis: A systematic review

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease that leads to inexorable motor decline and a median survival of three years from symptom onset. Surface EMG represents a major technological advance that has been harnessed in the development of novel neurophysiological biomarkers. We have systematically reviewed the current application of surface EMG techniques in ALS.

METHODS

We searched PubMed to identify 42 studies focusing on surface EMG and its associated analytical methods in the diagnosis, prognosis and monitoring of ALS patients.

RESULTS

A wide variety of analytical techniques were identified, involving motor unit decomposition from high-density grids, motor unit number estimation and measurements of neuronal hyperexcitability or neuromuscular architecture. Some studies have proposed specific diagnostic and prognostic criteria however clinical calibration in large ALS cohorts is currently lacking. The most validated method to monitor disease is the motor unit number index (MUNIX), which has been implemented as an outcome measure in two ALS clinical trials.

CONCLUSION

Surface EMG offers significant practical and analytical flexibility compared to invasive techniques. To capitalise on this fully, emphasis must be placed upon the multi-disciplinary collaboration of clinicians, bioengineers, mathematicians and biostatisticians.

SIGNIFICANCE

Surface EMG techniques can enrich effective biomarker development in ALS.

Motor unit innervation zone localization based on robust linear regression analysis

With the aim of developing a flexible and reliable procedure for superficial muscle innervation zone (IZ) localization, we proposed a method to estimate IZ location using surface electromyogram (EMG) based on robust linear regression. Regression lines were used to model the bidirectional propagation pattern of a single motor unit action potential (MUAP) and visualize the trajectory of the MUAP propagation. IZ localization was performed by identifying the origin of the bidirectional MUAP propagation. Robust linear regression and MUAP peak detection, combined with propagation phase reversal identification, may provide an efficient way to estimate IZ location. Our method offers high resolution in locating IZs based on simulation studies and experimental tests. Furthermore, our method is flexible and may also be applied using a relatively small number of EMG channels. A comparative study of the proposed method with the cross-correlation method for IZ localization was conducted. The results obtained with simulated MUAPs and measured spontaneous MUAPs in the biceps brachii muscle in six subjects (four males and two females, 57 ± 10 years old) with amyotrophic lateral sclerosis (ALS). Our method achieved estimation performance comparable to that obtained by using the cross-correlation method but with higher resolution. This study provides an accurate and practical method to estimate IZ location.

Mitigation of excessive fatigue associated with functional electrical stimulation

OBJECTIVE

Restoration of motor function in paralyzed limbs using functional electrical stimulation (FES) is undermined by rapid fatigue associated with artificial stimulation. Typically, single electrodes are used to activate muscles with FES. However, due to the highly distributed branching of muscle nerves, a single electrode may not be able to activate the entire array of motor axons supplying a muscle. Therefore, stimulating muscle with multiple electrodes might enable access to a larger volume of muscle and thereby reduce fatigue.

APPROACH

Accordingly, we compared the endurance times that ankle dorsiflexion could be sustained at 20% maximum voluntary force using feedback controlled stimulation (25 Hz) of human tibialis anterior (TA) using one or four percutaneous intramuscular electrodes. In addition, we measured endurance times in response to direct stimulation of the nerve supplying TA and during voluntary contraction. In all sessions involving electrical stimulation, an anesthetic nerve block proximal to the site of stimulation was used to isolate the effects of stimulation and alleviate discomfort.

MAIN RESULTS

Endurance time associated with stimuli delivered by a single intramuscular electrode (84  ±  19 s) was significantly smaller than that elicited by four intramuscular electrodes (232  ±  123 s). Moreover, endurance time in response to nerve stimulation (787  ±  201 s) was not significantly different that that produced during voluntary contraction (896  ±  272 s).

SIGNIFICANCE

Therefore, excessive fatigue associated with FES is probably due to the inability of conventional FES systems to enlist the full complement of motor axons innervating muscle and can be mitigated using multiple electrodes or nerve-based electrodes.

Detection radius of EMG for fasciculations: Empiric study combining ultrasonography and electromyography

OBJECTIVE

The aims of this study were to investigate the detection radius and sensitivity of EMG for fasciculations.

METHODS

Muscle ultrasonography was performed simultaneously to EMG recordings in patients with fasciculations in the context of amyotrophic lateral sclerosis. Ultrasonography and EMG parameters were analyzed for selected fasciculations.

RESULTS

A total of 381 fasciculations were detected by ultrasonography in 18 muscles of 10 patients. Out of these, 125 (33%) were EMG-negative. In contrast, none of the fasciculations detected by EMG were ultrasonography-negative. EMG detection probability decreased significantly with increasing distance from the center of the fasciculation. EMG detection rate was 98% when the EMG needle was located within the fasciculation and 50% at 7.75 mm distance from the fasciculation center. In addition, EMG detection depended significantly on cross-sectional area of the fasciculation and presence of neurogenic changes.

CONCLUSIONS

For detecting the same fasciculations, EMG is less sensitive than ultrasonography. EMG detection probability decreases sharply at a distance comparable to motor unit size.

SIGNIFICANCE

These results extend previous knowledge about superior sensitivity of ultrasonography for fasciculations. Moreover, our novel bimodal detection method provides first in vivo data about the EMG detection radius for fasciculations in a clinical setting.

Evaluation of a laser-based sensor for the diagnosis of neurological disorders

Involuntary muscle activities like fasciculations or tremor are an indication for several neurological disorders. However, currently used techniques for measuring those activities are limited due to their invasiveness, the unsuitability for measuring a whole body simultaneously and the lack of an objective measurement of amplitude and duration of muscle activity. Hence, we developed a new laser-based sensor for the remote quantification of muscle activity. In the present paper we show a basic evaluation of our system by reference to ultrasound measurements. Our results show the detection limits of our remote sensor technology in terms of fasciculation size and depth within the muscle. Those results will help us for a better interpretation of our measurement results and hold promise for the future development of our system.

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.