Time-Domain Statistical Analysis of EMG Signals for Muscular Dysfunction Detection
DOI:
https://doi.org/10.70112/ajes-2024.13.2.4242Keywords:
Electromyogram (EMG) Signals, Time-Domain Statistical Features, Amyotrophic Lateral Sclerosis (ALS), Neuromuscular Diseases, Muscular Activity AnalysisAbstract
Electromyogram (EMG) signals provide a visual representation of the electrical activity of muscles and serve as a key tool for analyzing muscular activity in subjects with paralysis and neuromuscular diseases. This study focuses on the analysis of EMG signals in individuals with amyotrophic lateral sclerosis (ALS), myopathy, and healthy subjects. Twelve statistical features in the time domain are extracted from the EMG signals of these subjects, and the significance of these features is tested using an F-test. The results show that all twelve features are statistically significant (p < 0.05) in distinguishing between normal, ALS, and myopathy conditions. These findings suggest that time-domain statistical features can be effectively used to analyze paralysis conditions, potentially aiding in the development of better treatment options. Since biomedical signals are continuous by nature, graphical representations of these signals, such as time-amplitude plots, are essential for the analysis of time-series data. The analysis of EMG signals in the time domain reveals important information about the variations in amplitude over time, providing insights into muscular dysfunction in various paralysis conditions.
References
S. Chopra, A. K. Tripathy, A. Alphonso, M. Carvalho, and C. Sancia, “Brain computer interface and electro stimulation of muscles to assist paralyzed patients in limited movement of hands,” 2017 International Conference on Nascent Technologies in Engineering (ICNTE), 2017, pp. 1-6, doi: 10.1109/ICNTE.2017.7947950.
J. Chawla and A. S. Sahni, “Peripheral Nervous System Anatomy,”Medscape, 2016. [Online]. Available: https://emedicine.medscape.com/article/1948687-overview
A. H. Ropper and R. H. Brown, Adams and Victor’s Principles of Neurology, 8th ed., Chapter 3, McGraw-Hill Medical Publishing Division.
B. S. C. Beh, B. M. Greenberg, T. Frohman, and E. M. Frohman,“Transverse myelitis,” Neurol. Clin., vol. 31, no. 1, pp. 79-138, Feb. 2013, doi: 10.1016/j.ncl.2012.09.008. [PubMed] PMID: 23186897; [PMC] PMCID: PMC7132741.
L. Wijesekera and N. Leigh, “Amyotrophic lateral sclerosis,”Orphanet J. Rare Dis., vol. 4, p. 3, 2009, doi: 10.1186/1750-1172-4-3.
J. Eske, “Paralysis,” Medical News Today, 2020. [Online]. Available: https://www.medicalnewstoday.com/articles/paralysis
H. Stubblefield, “Paralysis,” Healthline, 2018. [Online]. Available: https://www.healthline.com/health/paralysis#diagnosis
P. Konrad, The ABC of EMG: A Practical Introduction to Kinesiological Electromyography, ver. 1.0, Apr. 2005. [Online]. Available: https://hermanwallace.com/download/The_ABC_of_EMG_by_Peter_Konrad.pdf
R. Merletti and D. Farina, “Analysis of intramuscular electromyogram signals,” Phil. Trans. R. Soc. A, vol. 367, pp. 357-368, 2009, doi: 10.1098/rsta.2008.0235.
D. H. Sutherland, “The evolution of clinical gait analysis part I:Kinesiological EMG,” Gait Posture, vol. 14, pp. 61-70, 2001. [Online]. Available: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.501.2507&rep=rep1&type=pdf
V. Medved and M. Cifrek, “Kinesiological electromyography,” in Biomechanics in Applications, V. Klika, Ed. Rijeka, Croatia: InTech, 2009. [Online]. Available: https://books.google.co.in/books?id=ai-aDwAAQBAJ&lpg=PA349&ots=TIbKn6GzVe&dq=Clinical%20Or%20Diagnostic%20EMG%20and%20Kinesiological%20EMG&lr&pg=PA349#v=onepage&q&f=false
D. I. Rubin, “Needle electromyography: Basic concepts,” in Handbook of Clinical Neurology, vol. 160, P. H. Levin and P. Chauvel, Eds., Elsevier, 2019, pp. 243-256, doi: 10.1016/B978-0-444-64032-1.00016-3. [Online]. Available: https://www.sciencedirect.com/science/article/pii/B9780444640321000163
Slideplayer, “Image Source,” [Online]. Available: https://slideplayer.com/slide/12264889/
J. E. Kim et al., “Basic concepts of needle electromyography,” Ann. Clin. Neurophysiol., vol. 21, no. 1, pp. 7-12, Jan. 2019,doi: 10.14253/acn.2019.21.1.7.
Mayo Clinic, “EMG Test,” [Online]. Available: https://www.mayoclinic.org/tests-procedures/emg/about/pac-20393913?p=1.
D. R. Moynes et al., “Electromyography and motion analysis of the upper extremity in sports,” Phys. Ther., vol. 66, no. 12, pp. 1212-1219, Dec. 1986. [Online]. Available: https://citeseerx.ist.psu.edu/ viewdoc/download?Doi=10.1.1.909.2233&rep=rep1&type=pdf
Brigham and Women’s Hospital, “EMG Test,” [Online]. Available: https://www.brighamandwomens.org/medical-resources/emg-test
Medical News Today, “Who gets paralysis?” [Online]. Available: https://www.medicalnewstoday.com/articles/281472#who-gets-it
iStockphoto, “Amyotrophic lateral sclerosis (ALS) disease signs and symptoms,” [Online]. Available: https://www.istockphoto.com/vector/amyotrophic-lateral-sclerosis-als-disease-signs-and-symptoms-gm1129371731-298309042
Cleveland Clinic, “Myopathy,” [Online]. Available:https://my.clevelandclinic.org/health/diseases/17256-myopathy
D. C. Preston and B. E. Shapiro, Electromyography andNeuromuscular Disorders: Clinical-Electrophysiologic Correlations, 3rd ed., Elsevier Inc., 2013.
C. S. Md Rasedul I and M. H. R. Mohammad, “A comprehensive study on EMG feature extraction and classifiers,” Op. Acc. J. Bio Eng & Bio Sci, vol. 1, no. 1, pp. 17-26, 2018. [Online]. Available: https://lupinepublishers.com/biomedical-sciences-journal/fulltext/a-comprehensive-study-on-eMG-feature-extraction-and-classifiers.ID.000104.php
A. Krishna V and P. Thomas, “Classification of EMG signals using spectral features extracted from dominant motor unit action potential,” International Journal of Engineering and Advanced Technology, vol. 4, no. 5, pp. 196–200, 2015.
N. C. Joyce and G. T. Carter, “Electrodiagnosis in persons with amyotrophic lateral sclerosis,” PM R, vol. 5, no. 5 Suppl., pp. S89–S95, May 2013, doi: 10.1016/j.pmrj.2013.03.020.
S. Paganoni and A. Amato, “Electrodiagnostic evaluation of myopathies,” Physical Medicine and Rehabilitation Clinics of North America, vol. 24, no. 1, pp. 193–207, Feb. 2013, doi: 10.1016/j.pmr.2012.08.017.
M. Nikolic, Database of Clinical Signals, Rigshospitalet, Copenhagen, DK, dataset N2001. [Online]. Available: http://www.emglab.net.
S. V. Patel and S. S. L, “Prediction and analysis of muscular paralysis disease using Daubechies wavelet approach,” Asian Journal of Electrical Sciences, vol. 10, no. 2, pp. 6-15, Jul.-Dec. 2021, doi: 10.51983/ajes-2021.10.2.3036.
S. V. Patel and S. S. L, “Performance analysis of paralysis disease using machine learning,” Asian Journal of Computer Science and Technology, vol. 11, no. 1, pp. 21-34, 2022, doi: 10.51983/ajcst-2022.11.1.3242.
S. V. Patel and S. S. L, “Analysis of muscular paralysis using EMG signal with wavelet decomposition approach,” Asian Journal of Computer Science and Technology, vol. 11, no. 1, pp. 5-16, 2022, doi: 10.51983/ajcst-2022.11.1.3241.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Centre for Research and Innovation
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
