Techniques and applications of EMG: measuring motor units from structure to function

A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.

Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach

OBJECTIVE

Distinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.

METHODS

EMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).

RESULTS

Diagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.

CONCLUSIONS

An automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance.

SIGNIFICANCE

In the future, machine learning algorithms may help improve the diagnostic accuracy of EMG examinations.

A Functional Human-on-a-Chip Autoimmune Disease Model of Myasthenia Gravis for Development of Therapeutics

Myasthenia gravis (MG) is a chronic and progressive neuromuscular disease where autoantibodies target essential proteins such as the nicotinic acetylcholine receptor (nAChR) at the neuromuscular junction (NMJ) causing muscle fatigue and weakness. Autoantibodies directed against nAChRs are proposed to work by three main pathological mechanisms of receptor disruption: blocking, receptor internalization, and downregulation. Current in vivo models using experimental autoimmune animal models fail to recapitulate the disease pathology and are limited in clinical translatability due to disproportionate disease severity and high animal death rates. The development of a highly sensitive antibody assay that mimics human disease pathology is desirable for clinical advancement and therapeutic development. To address this lack of relevant models, an NMJ platform derived from human iPSC differentiated motoneurons and primary skeletal muscle was used to investigate the ability of an anti-nAChR antibody to induce clinically relevant MG pathology in the serum-free, spatially organized, functionally mature NMJ platform. Treatment of the NMJ model with the anti-nAChR antibody revealed decreasing NMJ stability as measured by the number of NMJs before and after the synchrony stimulation protocol. This decrease in NMJ stability was dose-dependent over a concentration range of 0.01-20 μg/mL. Immunocytochemical (ICC) analysis was used to distinguish between pathological mechanisms of antibody-mediated receptor disruption including blocking, receptor internalization and downregulation. Antibody treatment also activated the complement cascade as indicated by complement protein 3 deposition near the nAChRs. Additionally, complement cascade activation significantly altered other readouts of NMJ function including the NMJ fidelity parameter as measured by the number of muscle contractions missed in response to increasing motoneuron stimulation frequencies. This synchrony readout mimics the clinical phenotype of neurological blocking that results in failure of muscle contractions despite motoneuron stimulations. Taken together, these data indicate the establishment of a relevant disease model of MG that mimics reduction of functional nAChRs at the NMJ, decreased NMJ stability, complement activation and blocking of neuromuscular transmission. This system is the first functional human in vitro model of MG to be used to simulate three potential disease mechanisms as well as to establish a preclinical platform for evaluation of disease modifying treatments (etiology).

Changes in motor unit bioelectrical activity recorded at two different sites in a muscle

INTRODUCTION

The objective of this study was to compare the properties of bioelectrical signals of motor units recorded at different sites in the muscles of controls, patients with myopathy and patients with motor neuron disease (MND).

METHODS

Five controls, 10 patients with myopathy and 11 patients with MND were included. Electrophysiologic tests were performed in the biceps brachii (BB) muscle from two recording sites. Site 1 was near the belly of the muscle and Site 2 was 5cm distal from Site 1, near the tendon. Multi-motor unit potential (MUP) analysis, jitter analysis, and peak number count were calculated from the signals recorded using a concentric needle electrode (CN).

RESULTS

At Site 2, duration was longer, number of phases was higher and amplitudes were smaller in MUPs compared with those recorded at Site 1. This significant difference between recording site and patient groups was related to neurogenic muscles. Jitter analysis showed no significant difference except an intergroup difference between the patient groups and controls. The peak number calculated using the CN was greater when recorded from Site 1 in concordance with MUP analysis.

CONCLUSION

Duration of MUP was longer and amplitude was smaller when the recording electrode was placed distally along the muscle near the tendon in neurogenic muscles, probably related to increased temporal dispersion. However, changing the position of the needle did not provide further information in distinguishing myogenic muscles.

Myasthenia gravis

Myasthenia gravis (MG) is an autoimmune disease caused by antibodies against the acetylcholine receptor (AChR), muscle-specific kinase (MuSK) or other AChR-related proteins in the postsynaptic muscle membrane. Localized or general muscle weakness is the predominant symptom and is induced by the antibodies. Patients are grouped according to the presence of antibodies, symptoms, age at onset and thymus pathology. Diagnosis is straightforward in most patients with typical symptoms and a positive antibody test, although a detailed clinical and neurophysiological examination is important in antibody-negative patients. MG therapy should be ambitious and aim for clinical remission or only mild symptoms with near-normal function and quality of life. Treatment should be based on MG subgroup and includes symptomatic treatment using acetylcholinesterase inhibitors, thymectomy and immunotherapy. Intravenous immunoglobulin and plasma exchange are fast-acting treatments used for disease exacerbations, and intensive care is necessary during exacerbations with respiratory failure. Comorbidity is frequent, particularly in elderly patients. Active physical training should be encouraged.

Use of Digital Infrared Thermal Imaging in the Electromyography Clinic: A Case Series

Introduction: Foot drop often results from denervation of the dorsiflexor muscles in the leg. Neurological evaluation begins with lower extremity motor testing followed by electromyography needle electrode examination (EMG-NEE). We explored digital infrared thermography (IRT) as a complementary tool in diagnosing peripheral nerve disorders.

Methods: Using a digital IRT camera, we recorded differences in skin surface temperatures from affected and unaffected limbs in three patients with unilateral foot drop. Denervation in the affected limb was confirmed with EMG-NEE.

Results: IRT imaging revealed lower relative skin surface temperatures in regions of the leg corresponding to denervated dorsiflexor muscles for all three consecutive patients who presented to the EMG Clinic with foot drop. 

Conclusions: Denervation appears to cause a decrease in thermal energy output from affected muscle groups. Alongside the EMG and magnetic resonance imaging (MRI), IRT may have an important role in assessing the severity and prognosis of a nerve injury. This observation may have implications for chronic pain syndromes, such as complex regional pain syndrome (CRPS), in which thermal change is a diagnostic criterion.

Intramuscular EMG Decomposition Basing on Motor Unit Action Potentials Detection and Superposition Resolution

A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAPs using a peel-off approach and similarly assigned. The method is composed of six stages of analytical procedures: preprocessing, segmentation, alignment and feature extraction, clustering and refinement, supervised classification, and superimposed waveform resolution. The performance of the proposed decomposition framework was evaluated using both synthetic EMG signals and real recordings obtained from healthy and stroke participants. The overall detection rate of MUAPs was 100% for both synthetic and real signals. The average accuracy for synthetic EMG signals was 87.23%. Average assignment accuracies of 88.63 and 94.45% were achieved for the real EMG signals obtained from healthy and stroke participants, respectively. Results demonstrated the ability of the developed framework to decompose intramuscular EMG signals with improved accuracy and efficiency, which we believe will greatly benefit the clinical utility of EMG for the diagnosis and rehabilitation of motor impairments in stroke patients.

The effect of recording site on extracted features of motor unit action potential

Motor unit action potential (MUAP), which consists of individual muscle fiber action potentials (MFAPs), represents the electrical activity of the motor unit. The values of the MUAP features are changed by denervation and reinnervation in neurogenic involvement as well as muscle fiber loss with increased diameter variability in myopathic diseases. The present study is designed to investigate how increased muscle fiber diameter variability affects MUAP parameters in simulated motor units. In order to detect this variation, simulated MUAPs were calculated both at the innervation zone where the MFAPs are more synchronized, and near the tendon, where they show increased temporal dispersion. Reinnervation in neurogenic state increases MUAP amplitude for the recordings at both the innervation zone and near the tendon. However, MUAP duration and the number of peaks significantly increased in a case of myopathy for recordings near the tendon. Furthermore, of the new features, "number of peaks×spike duration" was found as the strongest indicator of MFAP dispersion in myopathy. MUAPs were also recorded from healthy participants in order to investigate the biological counterpart of the simulation data. MUAPs which were recorded near to tendon revealed significantly prolonged duration and decreased amplitude. Although the number of peaks was increased by moving the needle near to tendon, this was not significant.