DETERMINATION OF EPOCH LENGTH AND REGRESSION MODEL FOR 15-SECOND SEGMENT OF SEMG SIGNAL USED IN JOINT ANALYSIS OF ELECTROMYOGRAPHY SPECTRUM AND AMPLITUDE
DOI:
https://doi.org/10.11113/jt.v78.9445Keywords:
Regression line, sEMG, epoch, muscle activity, isometric contraction, JASAAbstract
Regression model is one of the techniques employed in Joint Analysis of Electromyography Spectrum and Amplitude (JASA) to investigate the behaviour of muscle fatigue indices. However, the analysis of the electromyography signal is influenced by the epoch length and regression model used. To meaningfully describe the behaviour of fatigue indices, this study was conducted to determine the appropriate epoch length and regression model for 15-second segment of electromyography signal. Ten subjects participated in this study. With their right forearm and upper arm formed an angle of 90 degree, the subjects were asked to hold a 2-kg dumbbell and stayed in that position for 2 minutes. Surface electromyography (sEMG) was used to record the signal from the biceps brachii muscle. Two fatigue indices were extracted: Root Mean Square (RMS) and Mean Frequency (MNF). The 120-second sEMG signal from each subject was then sliced into 8 segments (15 seconds each). In each segment, the effect of different epoch lengths (1-second, 3-second, and 5-second) was studied. Standard Error Estimate (SEE) was used to decide the suitable epoch length. The 3-second and 5-second epoch lengths were found to fit the regression model better (smaller SEE value). When 3-second and 5-second epoch lengths were applied in different regression models (linear and polynomial), polynomial regression was found to better estimate the behaviour of the fatigue indices (higher correlation coefficient). This study concludes that 3-second and 5-second epoch length can fit the polynomial regression well. However, fatigue behaviour (pattern of changes in fatigue indices) for every 15-second segment of sEMG signal is better described by JASA using polynomial regression with 3-second epoch length.
References
Mesin, L., Cescon, C., Gazzoni, M., Merletti, R., Rainoldi, A. 2009. A Bi-Dimensional Index for the Selective Assessment of Myoelectric Manifestations of Peripheral and Central Muscle Fatigue. Journal of Electromyography and Kinesiology. 19: 851-863.
Enoka, R. M., Duchateau, J. 2008. Muscle Fatigue: What, Why and How It Influences Muscle Function. Journal of Physiology. 586(1): 11-23.
Hakonen M, Piitulainen H, and Visala A. 2015. Current State of Digital Signal Processing in Myoelectric Interfaces and Related Applications. Biomedical Signal Processing and Control. 18: 334-359.
González-Izal, M., Malanda, A., Gorostiaga, E., Izquierdo, M. 2012. Electromyographic Models to Assess Muscle Fatigue. Journal of Electromyography and Kinesiology. 22(4): 501-512.
Vøllestad, N. K. 1997. Measurement of Human Muscle Fatigue. Journal of Neuroscience Methods. 74: 219-227.
Luttmann, A., Jäger, M., Sökeland, J., Laurig, W. 1996. Electromyographic Study on Surgeons In Urology. Part II: Determination of Muscular Fatigue. Ergonomics. 39: 298-313.
Luttmann, A., Jäger, M., Laurig, W. 2000. Electromyographical Indication of Muscular Fatigue in Occupational Field Studies. International Journal of Industrial Ergonomics. 25: 645-660.
Soylu, A. R., Arpinar-Avsar, P. 2010. Detection of Surface Electromyography Recording Time Interval Without Muscle Fatigue Effect for Biceps Brachii Muscle During Maximum Voluntary Contraction. Journal of Electromyography and Kinesiology. 20: 773-776.
Doix, A. M., Gulliksen, A., Brændvik, S. M., Roeleveld, K. 2013. Fatigue and Muscle Activation During Submaximal Elbow Flexion in Children with Cerebral Palsy. Journal of Electromyography and Kinesiology. 23: 721-726.
Marras, W. S., and Davis, K. G. 2001. Anon-MVC EMG Normalization for the Trunk Musculature: Part 1. Method Development. Journal of Electromyography and Kinesiology. 11: 1-9.
Farfán FD, Politti JC, and Felice CJ. 2010. Evaluation of EMG Processing Technique using Information Theory. Biomedical Engineering Online. 9: 72.
Hendrix, C. R., Housh, T. J., Johnson, G. O., Mielke, M., Camic, C. L. 2009. A New EMG Frequency-Based Fatigue Threshold Test. Journal of Neuroscience Methods. 181: 45-51.
Oliveira, A. D. S. C., Gonçalves, M. 2009. EMG Amplitude And Frequency Parameters of Muscular Activity: Effect of Resistance Training Based on Electromyographic Fatigue Threshold. Journal of Electromyography and Kinesiology. 19: 295-303.
Potvin, J. R., Bent, L. R. 1996. A Validation of Technique using Surface EMG Signals from Dynamic Contraction to Quantify Muscle Fatigue During Repetitive Tasks. Journal of Electromyography and Kinesiology. 7(2): 131-139.
Chua, Y. P. 2013. Mastering Research Statistics. Malaysia: Mc Graw Hill Education. 246-282.
Brown, J. D. 1999. Standard Error vs. Standard Error of Measurement. Shiken: JALT Testing Evaluation SIG newsletter. 3(1): 20-25.
Vera-Garcia, F. J., Moreside, J. M., McGill, S. M. 2010. MVC Technique to Normalize Trunk Muscle EMG in Healthy Women. Journal of Electromyography and Kinesiology. 20: 10-16
Danion, F., GallÄ—a, C. 2004. The Relationship between Force Magnitude, Force Steadiness, and Muscle Co-Contraction in The Thumb During Precision Grip. Neuroscience Letters. 368(2): 176-180.
Thongpanja, S., Phinyomark, A., Phukpattaranont, P., Limsakul, C. 2012. A Feasibility Study of Fatigue and Muscle Contraction Indices Based on EMG Time-Dependent Spectral Analysis. Procedia Engineering. 32: 179-186.
Redfern, M. S., Hughes, R. E., Chaffin, D. B. 1993. High-Pass Filtering to Remove Electrocardiographic Interference from Torso EMG Recordings. Clinical Biomechanics. 8: 44-48
Konrad, P. 2005. The ABC of EMG: A Practical Introduction to Kinesiological Electromyography. USA: Noraxon Inc.1: 26-28.
Cifrek, M., Medved, V., Tonković, S., Ostojić, S. 2009. Surface EMG Based Muscle Fatigue Evaluation in Biomechanics. Clinical Biomechanics. 24: 327-340.
Nordin, M. and Frankel, V. H. 2012. Basic Biomechanics of the Musculoskeletal System. 4th edition. Philadelphia: Lippincott Williams & Wilkins. 160-165.
Bruning, J. L., Kintz, B. L. 1997. Computational Handbook of Statistics. 4th edition. New York: Addison-Wesley Educational Publishers Inc.
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