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Possessing a discrete functional repertoire, the anterior horn cell can be in one of two electrophysiological states: on or off. Usually under tight regulatory control by the central nervous system, a hierarchical network of these specialist neurons ensures muscular strength is coordinated, gradated and adaptable. However, spontaneous activation of these cells and their axons can result in abnormal muscular twitching. The muscular twitch is the common building block of several distinct clinical patterns, namely fasciculation, myokymia and neuromyotonia. When attempting to distinguish these entities electromyographically, their unique temporal and morphological profiles must be appreciated. Detection and quantification of burst duration, firing frequency, multiplet patterns and amplitude are informative. A common feature is their persistence during sleep. In this review, we explain the accepted terminology used to describe the spontaneous phenomena of motor hyperexcitability, highlighting potential pitfalls amidst a bemusing and complex collection of overlapping terms. We outline the relevance of these findings within the context of disease, principally amyotrophic lateral sclerosis, Isaacs syndrome and Morvan syndrome. In addition, we highlight the use of high-density surface electromyography, suggesting that more widespread use of this non-invasive technique is likely to provide an enhanced understanding of these motor hyperexcitability syndromes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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., Competing Interests: Declaration of Competing Interest The authors declared that there are no conflicts of interest., (Copyright © 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

• A novel preprocessing step removes the need for manual selection of relaxed surface EMG data. • SPiQE provides reliable fasciculation analysis from raw thirty-minute recordings in ALS. • This paves the way for clinical calibration of a potential novel biomarker of disease progression. Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). The Surface Potential Quantification Engine (SPiQE) is a novel analytical tool to identify fasciculation potentials from high-density surface electromyography (HDSEMG). This method was accurate on relaxed recordings amidst fluctuating noise levels. To avoid time-consuming manual exclusion of voluntary muscle activity, we developed a method capable of rapidly excluding voluntary potentials and integrating with the established SPiQE pipeline. Six ALS patients, one patient with benign fasciculation syndrome and one patient with multifocal motor neuropathy underwent monthly thirty-minute HDSEMG from biceps and gastrocnemius. In MATLAB, we developed and compared the performance of four Active Voluntary IDentification (AVID) strategies, producing a decision aid for optimal selection. Assessment of 601 one-minute recordings permitted the development of sensitive, specific and screening strategies to exclude voluntary potentials. Exclusion times (0.2–13.1 minutes), processing times (10.7–49.5 seconds) and fasciculation frequencies (27.4–71.1 per minute) for 165 thirty-minute recordings were compared. The overall median fasciculation frequency was 40.5 per minute (10.6–79.4 IQR). We hereby introduce AVID as a flexible, targeted approach to exclude voluntary muscle activity from HDSEMG recordings. Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health. [ABSTRACT FROM AUTHOR]

• SPiQE combines serial high-density surface EMG with an innovative signal-processing methodology. • SPiQE identifies fasciculations in ALS patients with high sensitivity and specificity. • The optimal noise-responsive model achieves an average classification accuracy of 88%. Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool. Six ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model's predictive ability. By applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/− 0.2 SEM), specificity of 91.6% (+/− 0.1 SEM) and classification accuracy of 87.9% (+/− 0.1 SEM). An amplitude exclusion threshold (100 μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p = 0.394). We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health. [ABSTRACT FROM AUTHOR]

• Aligned, automated outputs from muscle ultrasound and surface electromyography improved characterisation of fasciculations. • The latency between electrical and mechanical peaks was prolonged in amyotrophic lateral sclerosis. • Such insight into impaired excitation–contraction coupling could be translated into a novel biomarker of disease. In amyotrophic lateral sclerosis (ALS), motor neurons become hyperexcitable and spontaneously discharge electrical impulses causing fasciculations. These can be detected by two noninvasive methods: high-density surface electromyography (HDSEMG) and muscle ultrasonography (MUS). We combined these methods simultaneously to explore the electromechanical properties of fasciculations, seeking a novel biomarker of disease. Twelve ALS patients and thirteen healthy participants each provided up to 24 minutes of recordings from the right biceps brachii (BB) and gastrocnemius medialis (GM). Two automated algorithms (Surface Potential Quantification Engine and a Gaussian mixture model) were applied to HDSEMG and MUS data to identify correlated electromechanical fasciculation events. We identified 4,197 correlated electromechanical fasciculation events. HDSEMG reliably detected electromechanical events up to 30 mm below the skin surface with an inverse correlation between amplitude and depth in ALS muscles. Compared to Healthy-GM muscles (mean = 79.8 ms), electromechanical latency was prolonged in ALS-GM (mean = 108.8 ms; p = 0.0458) and ALS-BB (mean = 112.0 ms; p = 0.0128) muscles. Electromechanical latency did not correlate with disease duration, symptom burden, sum muscle power score or fasciculation frequency. Prolonged fasciculation electromechanical latency indicates impairment of the excitation–contraction coupling mechanism, warranting further exploration as a potential novel biomarker of disease in ALS. This study points to an electromechanical defect within the muscles of ALS patients. [ABSTRACT FROM AUTHOR]