Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology · 2026
Reference values for near fiber EMG for the diagnosis of neuromuscular disorders.
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Garnés-Camarena Oscar, Mahíllo-Fernández Ignacio, Lorenzo Oscar, Stashuk Daniel W
Аннотация
OBJECTIVE: Quantitative EMG improves diagnostic accuracy and objectivity of EMG examinations. Decomposition-based Quantitative EMG (DQEMG) is a high-quality method to quantify motor unit potential (MUP) train features, and includes novel algorithms to extract near-fiber EMG (NF-EMG) features related to motor unit (MU) electrophysiological temporal structure and instability. This study characterized reference values for NF-EMG features in healthy muscles.
METHODS: EMG signals were acquired using concentric needle electrodes from seven commonly examined muscles (330 studies, 196 patients) following standard protocols. DQEMG software extracted MUP trains and calculated both conventional MUP and NF-EMG features. Threshold-of-normality values were defined based on the 2.5 and 97.5 percentiles. For each NF-EMG feature, individual and mean upper and lower thresholds, as well as maximum incidence of abnormal values in healthy muscles, were determined.
RESULTS: NF-EMG feature thresholds-of-normality were consistent, and value distributions showed low inter-subject and inter-muscle variability. The concurrence of increased temporal dispersion and instability in a healthy population was minimal.
CONCLUSIONS: NF-EMG temporal structure and instability features, extracted from conventional EMG signals, are tightly clustered in healthy muscles and could help detect early neurophysiological changes before conventional MUP features exceed threshold-of-normality values. Thus, NF-EMG could be a valuable tool for diagnosing neuromuscular disorders.
METHODS: EMG signals were acquired using concentric needle electrodes from seven commonly examined muscles (330 studies, 196 patients) following standard protocols. DQEMG software extracted MUP trains and calculated both conventional MUP and NF-EMG features. Threshold-of-normality values were defined based on the 2.5 and 97.5 percentiles. For each NF-EMG feature, individual and mean upper and lower thresholds, as well as maximum incidence of abnormal values in healthy muscles, were determined.
RESULTS: NF-EMG feature thresholds-of-normality were consistent, and value distributions showed low inter-subject and inter-muscle variability. The concurrence of increased temporal dispersion and instability in a healthy population was minimal.
CONCLUSIONS: NF-EMG temporal structure and instability features, extracted from conventional EMG signals, are tightly clustered in healthy muscles and could help detect early neurophysiological changes before conventional MUP features exceed threshold-of-normality values. Thus, NF-EMG could be a valuable tool for diagnosing neuromuscular disorders.