Applications of electromyography in Amyotrophic Lateral Sclerosis: A systematic review

Clinical Snapshot

Key Findings

• P Value: Not reported at review level — individual study statistics not synthesised

• Effect Size: Not reported — no meta-analysis performed; findings are narrative and descriptive only

• Primary Outcome: sEMG parameters — including muscle fatigue indices, fasciculation patterns, motor unit number estimation (MUNE/MUNIX), motor unit firing rates, and signal complexity — demonstrated sensitivity to ALS disease progression and potential utility for early diagnosis, phenotypic stratification, and functional monitoring

• Nnt Or Sensitivity: Not reported — no diagnostic accuracy metrics (sensitivity, specificity, AUC) pooled across studies; individual study performance data not summarised in the abstract

• Confidence Interval: Not reported — no quantitative synthesis conducted

Clinical Application

sEMG is non-invasive, relatively low-cost compared to needle EMG, and increasingly portable — making it feasible for both specialist clinic and research settings. However, the lack of standardised acquisition protocols (sampling frequencies ranging 500–3000 Hz across studies) and the need for advanced signal processing expertise (including machine learning pipelines) present significant implementation barriers in routine clinical practice. High-density sEMG systems require specialist equipment and operator training. ALS (also termed Motor Neurone Disease, MND, in Australian clinical practice) affects approximately 2,000 Australians at any given time, with ~400 new diagnoses annually (MND Australia). Neurophysiological assessment, including needle EMG, is a cornerstone of ALS diagnosis under the Awaji-Shima criteria and is Medicare-rebatable (MBS Item 11005 and related items). Surface EMG is not currently a standard diagnostic tool in Australian ALS care pathways and is not separately listed on the MBS for this indication. The TGA has not approved specific sEMG devices for ALS diagnosis. RACGP and Neurological Society of Australia guidelines do not currently recommend sEMG as a standalone diagnostic or monitoring tool for ALS. This review's findings may inform future research protocols at Australian MND clinics (e.g., Royal Melbourne Hospital, Westmead, Royal Brisbane) but do not yet support changes to PBS-funded care or RACGP referral pathways. The increasing use of machine learning-based sEMG analysis aligns with Australia's National Digital Health Strategy priorities but requires regulatory evaluation by the TGA as a Software as a Medical Device (SaMD) before clinical deployment. Adults with confirmed ALS diagnosis across disease stages and phenotypes (bulbar-onset and limb-onset); potentially applicable to patients undergoing neuromuscular assessment in specialist neurology or neurophysiology settings. Applicability to specific ALS subtypes or early/pre-symptomatic populations requires further study.

Abstract

This systematic review examined the use of surface electromyography (sEMG) for the neuromuscular assessment of individuals with Amyotrophic Lateral Sclerosis (ALS), focusing on clinical parameters, the muscle groups evaluated, acquisition protocols, technical properties of the recording systems, integration with other technologies, and signal processing strategies. We included observational studies that applied sEMG to individuals diagnosed with ALS, with or without comparison to healthy controls, and without restrictions on publication year. The analyses included signals recorded at rest and during voluntary contractions, with or without the use of biofeedback. Most studies employed conventional or high-density surface electrodes, with sampling frequencies ranging from 500 Hz to 3000 Hz. The results showed that the primary parameters assessed were muscle fatigue, fasciculation patterns, the number of motor units (MUNE/MUNIX), motor unit firing rates, and signal complexity. These parameters demonstrated sensitivity to disease progression and may contribute to early diagnosis, phenotypic stratification, and functional monitoring of ALS. Additionally, the studies highlighted the increasing use of advanced computational approaches, such as machine learning, for feature extraction and automated classification. In conclusion, sEMG is a promising tool for functional assessment in ALS, with the potential to improve diagnostic accuracy and support new therapeutic strategies based on electrophysiological biomarkers. However, despite technological advances, the included studies displayed substantial methodological heterogeneity and limited protocol standardization. Integration with other neurophysiological modalities also remains underexplored, despite its significant clinical potential.

References

1. 1.Fernandes, A. P. M., Bertucci Borges, L. H., Holanda, L. J. de, Bezerra, B. H. E. S., Lopes, A. C. S. M., Silva, M. C. F. da, Valentim, R. A. de M., Bougrain, L., Vasiljevic, G. A. M., & Rodrigues Lindquist, A. R. (2026). Applications of electromyography in Amyotrophic Lateral Sclerosis: A systematic review. PLoS ONE. https://doi.org/10.1371/journal.pone.0350029