OBJECTIVE: To define reference values for motor unit (MU) recruitment during needle EMG of six commonly examined muscles at low to moderate contraction.
METHODS: Needle examination was performed for each muscle in a total of 111 subjects without neuromuscular disorders. Fastest firing rates and recruitment ratios (RRs) were calculated in at least 5 sites within each muscle. Upper limits of normal based on 97th percentile for fastest MU firing rates and RRs were calculated for each muscle. The means of fastest firing rates were compared among muscles using the Friedman and Wilcoxon signed rank tests.
RESULTS: The upper limits of normal were 12-15 Hz for fastest firing rates and were slightly higher in the deltoid and triceps than the other muscles.
CONCLUSIONS: Firing rates >15 Hz recorded at multiple sites within a single muscle exceed the 97th percentile of normal subjects and may suggest reduced MU recruitment. In some muscles, rates >12 Hz might support mildly reduced recruitment. Recruitment ratios varied depending on number of firing MUs, whereas the fastest firing MU rate did not.
CONCLUSIONS: The determination of reference values for fastest MU firing rates can help to identify mild reduction in recruitment with more accuracy.

Electrodiagnostic (EDX) testing plays an important role in confirming a mononeuropathy, localizing the site of nerve injury, defining the pathophysiology, and assessing the severity and prognosis. The combination of nerve conduction studies (NCS) and needle electromyography findings provides the necessary information to fully assess a nerve. The pattern of NCS abnormalities reflects the underlying pathophysiology, with focal slowing or conduction block in neuropraxic injuries and reduced amplitudes in axonotmetic injuries. Needle electromyography findings, including spontaneous activity and voluntary motor unit potential changes, complement the NCS findings and further characterize chronicity and degree of axon loss and reinnervation. EDX is used as an objective marker to follow the progression of a mononeuropathy over time.

This scoping review provides an overview of artificial intelligence (AI), including machine and deep learning techniques, in the interpretation of clinical needle electromyography (nEMG) signals.
A comprehensive search of Medline, Embase and Web of Science was conducted to find peer-reviewed journal articles. All papers published after 2010 were included. The methodological quality of the included studies was assessed with CLAIM (checklist for artificial intelligence in medical imaging).
51 studies were identified that fulfilled the inclusion criteria. 61% used open-source EMGlab data set to develop models to classify nEMG signal in healthy, amyotrophic lateral sclerosis (ALS) and myopathy (25 subjects). Only two articles developed models to classify signals recorded at rest. Most articles reported high performance accuracies, but many were subject to bias and overtraining.
Current AI-models of nEMG signals are not sufficient for clinical implementation. Suggestions for future research include emphasizing the need for an optimal training and validation approach using large datasets of clinical nEMG data from a diverse patient population.
The outcomes of this study and the suggestions made aim to contribute to developing AI-models that can effectively handle signal quality variability and are suitable for daily clinical practice in interpreting nEMG signals.

The type, distribution pattern and time course of spontaneous muscular activity are important for the diagnostics of neuromuscular diseases in the clinical practice. In neurogenic lesions with motor axonal involvement, pathologic spontaneous activity (PSA) is usually reliably detectable by needle electromyography (EMG) 2-4 weeks after occurrence of the lesion. The distribution pattern correlates with the lesion location. The focus of the present work is the description of the different forms of PSA in myogenic diseases.
UNASSIGNED: Art, Verteilungsmuster und der zeitliche Verlauf muskulärer Spontanaktivität sind für die Diagnostik neuromuskulärer Krankheiten im klinischen Alltag bedeutsam. Bei neurogenen Läsionen mit motorisch axonaler Beteiligung ist pathologische Spontanaktivität (PSA) meist 2 bis 4 Wochen nach Läsionsbeginn mittels Nadelelektromyographie sicher fassbar. Das Verteilungsmuster korreliert dabei mit dem Läsionsort. Schwerpunkt der vorliegenden Arbeit liegt in der Darstellung der unterschiedlichen PSA-Verteilungsmuster bei myogenen Erkrankungen.

BACKGROUND: Recently, there have been significant advances in the treatment of spinal muscular atrophy (SMA). Although clinical improvement in patients with SMA after the treatment has been reported, changes in electrophysiological findings, especially needle electromyography (EMG), have rarely been reported. Herein, we report the posttreatment changes in EMG and nerve conduction study findings over time in two patients with SMA type I.
METHODS: Patient 1: A 2.5-year-old girl was diagnosed with SMA type I at 1 month of age. She received nusinersen four times and onasemnogene abeparvovec (OA) was administered at 6 months of age. The compound muscle action potential (CMAP) amplitudes of the median and tibial nerves increased over time. The needle EMG after the treatment showed high-amplitude motor unit potentials (MUPs) suggestive of reinnervation during voluntary contraction, which were not seen before the treatment. However, fibrillation potentials at rest were still seen after the treatment. Patient 2: A 2-year-old girl was diagnosed with SMA type I at 6 months of age. She had received nusinersen two times and OA was administered at 7 months of age. The CMAP amplitudes and the MUPs presented similar changes as presented in Case 1.
CONCLUSIONS: This is the first report on the changes in needle EMG findings after treatment in patients with SMA type I. These findings suggested that peripheral nerve reinnervation occurred after the treatment, although active denervation was still present. The accumulation of these findings will be important for evaluating the effectiveness of treatment for SMA in the future.

OBJECTIVE: Needle electromyography (EMG) and muscle ultrasound can be used to evaluate patients with suspected neuromuscular disorders. The relation between muscle ultrasound pathology and the corresponding needle EMG findings is unknown. In this study we compared the results of concurrent ultrasound and needle EMG examinations in patients suspected of a neuromuscular disorder.
METHODS: Retrospective data from 218 patients with pairwise ultrasound and EMG results of 796 muscles were analyzed. We compared overall quantitative and visual muscle ultrasound results to EMGs with neurogenic and myopathic abnormalities and assessed the congruency of both methods in the different clinical diagnosis categories.
RESULTS: In muscles of patients with a neuromuscular disorder, abnormalities were found with EMG in 71.8%, and quantitative and visual muscle ultrasound results were abnormal in 19.3% and 35.4% respectively. In muscles with neurogenic EMG abnormalities, quantitative and visual muscle ultrasound results were abnormal in 18.9% versus 35.6%, increasing up to 43.7% versus 87.5% in muscles with the most pronounced signs of denervation. Congruency of EMG and ultrasound was better for more proximal and cranial muscles than for muscles in the hand and lower limb.
CONCLUSIONS: Needle EMG and muscle ultrasound typically produce disparate results and identify different aspects of muscle pathology. Muscle ultrasound seems less suited for detecting mild neurogenic abnormalities. As the severity of neurogenic needle EMG abnormalities increased, muscle ultrasound abnormalities were also increasingly found. Visual analysis seems better suited than grayscale quantification for detecting neurogenic abnormalities.

OBJECTIVE: To explore the changes of elbow flexor muscle strength after musculocutaneous nerve injury and its correlation with needle electromyography (nEMG) parameters.
METHODS: Thirty cases of elbow flexor weakness caused by unilateral brachial plexus injury (involving musculocutaneous nerve) were collected. The elbow flexor muscle strength was evaluated by manual muscle test (MMT) based on Lovett Scale. All subjects were divided into Group A (grade 1 and grade 2, 16 cases) and Group B (grade 3 and grade 4, 14 cases) according to their elbow flexor muscle strength of injured side. The biceps brachii of the injured side and the healthy side were examined by nEMG. The latency and amplitude of the compound muscle action potential (CMAP) were recorded. The type of recruitment response, the mean number of turns and the mean amplitude of recruitment potential were recorded when the subjects performed maximal voluntary contraction. The quantitative elbow flexor muscle strength was measured by portable microFET 2 Manual Muscle Tester. The percentage of residual elbow flexor muscle strength (the ratio of quantitative muscle strength of the injured side to the healthy side) was calculated. The differences of nEMG parameters, quantitative muscle strength and residual elbow flexor muscle strength between the two groups and between the injured side and the healthy side were compared. The correlation between elbow flexor manual muscle strength classification, quantitative muscle strength and nEMG parameters was analyzed.
RESULTS: After musculocutaneous nerve injury, the percentage of residual elbow flexor muscle strength in Group B was 23.43% and that in Group A was 4.13%. Elbow flexor manual muscle strength classification was significantly correlated with the type of recruitment response, and the correlation coefficient was 0.886 (P<0.05). The quantitative elbow flexor muscle strength was correlated with the latency and amplitude of CMAP, the mean number of turns and the mean amplitude of recruitment potential, and the correlation coefficients were -0.528, 0.588, 0.465 and 0.426 (P<0.05), respectively.
CONCLUSIONS: The percentage of residual elbow flexor muscle strength can be used as the basis of muscle strength classification, and the comprehensive application of nEMG parameters can be used to infer quantitative elbow flexor muscle strength.
目的: 研究肌皮神经损伤者的屈肘肌力变化及其与针极肌电图指标的相关性。方法: 收集单侧臂丛神经损伤（累及肌皮神经）致屈肘肌力降低案例30例，根据Lovett肌力分级标准进行徒手肌力评定，并将30例受试者分为A组（伤侧徒手肌力1级和2级，16例）和B组（伤侧徒手肌力3级和4级，14例）。对所有受试者的伤侧和健侧肱二头肌行针极肌电图检测。记录肱二头肌复合肌肉动作电位（compound muscle action potential，CMAP）的潜伏期和波幅。受试者进行最大随意收缩时，记录募集反应类型以及募集电位的平均转折数和平均波幅，并采用microFET 2便携式肌力测试仪检测屈肘定量肌力。计算残存屈肘肌力百分比（伤侧与健侧定量肌力的比值）。比较各针极肌电图指标、定量肌力、残存屈肘肌力百分比在两组之间和组内伤侧与健侧之间的差异。分别分析屈肘徒手肌力分级、定量肌力与针极肌电图指标的相关性。结果: 肌皮神经损伤后B组的伤侧残存屈肘肌力百分比为23.43%，A组的伤侧残存屈肘肌力百分比为4.13%。伤侧徒手肌力分级与募集反应类型相关，相关系数为0.886（P<0.05）。伤侧定量肌力与CMAP潜伏期、波幅以及募集电位的平均转折数和平均波幅均相关，相关系数分别为-0.528、0.588、0.465、0.426（P<0.05）。结论: 残存屈肘肌力百分比可作为肌力分级的依据，针极肌电图指标的综合分析可用于推断屈肘定量肌力。.

In patients with traumatic radial nerve injury (RNI), the chance of spontaneous recovery must be balanced against the benefits of early surgical reconstruction. We aimed to explore the time-specific value of needle electromyography (NEMG) to diagnose nerve lesion severity.
In this retrospective diagnostic accuracy study at Leiden Nerve Center, patients at least 12 years of age with RNI caused by fractures or fracture treatment were included. The sensitivity and specificity of the patients\' first NEMG examination were assessed, stratified by the timing after the nerve injury. The absence of motor unit potentials (MUPs) in muscles distal to the nerve lesion was considered a positive test result. Lesion severity was dichotomized to moderate injury (spontaneous Medical Research Council grade ≥3 recovery) or severe injury (poor spontaneous recovery or surgical confirmation of a mainly neurotmetic lesion).
Ninety-five patients were included in our study. The sensitivity of NEMG to detect severe RNI was 75.0% (3 of 4) in the fourth, 66.7% (2 of 3) in the fifth, and 66.7% (2 of 3) in the sixth month after the nerve injury. The specificity in the first to the sixth month was 0.0% (0 of 1), 50.0% (2 of 4), 77.3% (17 of 22), 95.5% (21 of 22), 95.8% (23 of 24), and 100.0% (12 of 12), respectively.
The specificity of NEMG is higher than 95% and therefore clinically relevant from the fourth month after the nerve injury onward. Absence of MUPs at this time can be considered an indication to plan nerve exploration. Moreover, the presence of MUPs on NEMG does not completely exclude the necessity for surgical reconstruction.

OBJECTIVE: Neuromuscular disorders are diseases that damage our ability to control body movements. Needle electromyography (nEMG) is often used to diagnose neuromuscular disorders, which is an electrophysiological test measuring electric signals generated from a muscle using an invasive needle. Characteristics of nEMG signals are manually analyzed by an electromyographer to diagnose the types of neuromuscular disorders, and this process is highly dependent on the subjective experience of the electromyographer. Contemporary computer-aided methods utilized deep learning image classification models to classify nEMG signals which are not optimized for classifying signals. Additionally, model explainability was not addressed which is crucial in medical applications. This study aims to improve prediction accuracy, inference time, and explain model predictions in nEMG neuromuscular disorder classification.
METHODS: This study introduces the nEMGNet, a one-dimensional convolutional neural network with residual connections designed to extract features from raw signals with higher accuracy and faster speed compared to image classification models from previous works. Next, the divide-and-vote (DiVote) algorithm was designed to integrate each subject\'s heterogeneous nEMG signal data structures and to utilize muscle subtype information for higher accuracy. Finally, feature visualization was used to identify the causality of nEMGNet diagnosis predictions, to ensure that nEMGNet made predictions on valid features, not artifacts.
RESULTS: The proposed method was tested using 376 nEMG signals measured from 57 subjects between June 2015 to July 2020 in Seoul National University Hospital. The results from the three-class classification task demonstrated that nEMGNet\'s prediction accuracy of nEMG signal segments was 62.35%, and the subject diagnosis prediction accuracy of nEMGNet and the DiVote algorithm was 83.69 %, over 5-fold cross-validation. nEMGNet outperformed all models from previous works on nEMG diagnosis classification, and heuristic analysis of feature visualization results indicate that nEMGNet learned relevant nEMG signal characteristics.
CONCLUSIONS: This study introduced nEMGNet and DiVote algorithm which demonstrated fast and accurate performance in predicting neuromuscular disorders based on nEMG signals. The proposed method may be applied in medicine to support real-time electrophysiologic diagnosis.

In traumatic nerve lesions (TNLs), motor unit potentials (MUPs) may be difficult to detect in early injury. Ultrasound-guided electromyography (US-EMG) can aid in identifying areas of muscle activation, but its sensitivity can be improved. In this study we compare the sensitivity of US-EMG alone with US-EMG after peripheral nerve stimulation (NC-US-EMG) to better identify active muscle regions.
In this prospective study, 32 patients with severe TNLs were evaluated with standard EMG (ST-EMG), US-EMG, and NC-US-EMG at baseline (T0), after 2 to 3 months (T1), and after 5 to 6 months (T2).
NC-US-EMG was more sensitive in detecting MUPs compared with US-EMG and ST-EMG at T0 (19 patients vs 14 and 5 patients, respectively). In addition, both US-guided techniques were more sensitive than ST-EMG in detecting MUPs (ST-EMG vs US-EMG: P = .014; ST-EMG vs NC-US-EMG: P = .003). At T1, ST-EMG remained less sensitive NC-US-EMG (P = .019). No significant differences were observed among the three techniques at T2.
In the evaluation of severe TNLs, the combination of peripheral nerve stimulation and US increases the sensitivity of EMG for MUP detection at baseline and 2 to 3 months postinjury.