Abstract
Biomechanical analysis of human movement plays an essential role in understanding functional changes in people with Amyotrophic Lateral Sclerosis (ALS), providing information on muscle impairment. Studies suggest that surface electromyography (sEMG) may be able to quantify muscle activity, identify levels of fatigue, assess muscle strength, and monitor variation in limb movement. In this article, a systematic review protocol will analyze the psychometric properties of the sEMG regarding the clinical data on the skeletal muscles of people with ALS. This protocol uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological tool. A specific field structure was defined to reach each phase. Nine scientific databases (PubMed, Web of Science, Embase, Elsevier, IEEE, Google Scholar, SciELO, PEDro, LILACS E CENTRAL) were searched. The framework developed will extract data (i.e. study information, sample information, sEMG information, intervention, and outcomes) from the selected studies using a rigorous approach. The data will be described quantitatively using frequency and trend analysis methods, and heterogeneity between the included studies will be assessed using the I2 test. The risk of bias will be summarized using the most recent prediction model risk of bias assessment tool. Be sure to include relevant statistics here, such as sample sizes, response rates, P values or Confidence Intervals. Be specific (by stating the value) rather than general (eg, “there were differences between the groups”). This protocol will map out the construction of a systematic review that will identify and synthesize the advances in movement analysis of people with ALS through sEMG, using data extracted from articles.
Funder
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Publisher
Public Library of Science (PLoS)
Reference32 articles.
1. The effect of EMG biofeedback on lower extremity functions in hemiplegic patients;G Dost Sürücü;Acta Neurologica Belgica,2021
2. Quantitative measurement of rigidity in Parkinson’s disease: a systematic review;MdR Ferreira-Sánchez;Sensors,2020
3. Surface electromyography signal processing and classification techniques;RH Chowdhury;Sensors,2013
4. Ricamato AL, Absher RG, Moffroid MT, Tranowski JP. A time-frequency approach to evaluate electromyographic recordings. In: [1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems. IEEE; 1992. p. 520–527.