Analysis of Electromyography (EMG) Signal Processing with Filtering Techniques
DOI:
https://doi.org/10.56741/jnest.v3i02.524Keywords:
Electromyography, EMG, Filtering Techniques, Signal ProcessingAbstract
The paper presents the Analysis of Electromyography (EMG) Signal Processing with Filtering Techniques. The problem in this study is how to consider the filtering techniques for fundamental EMG signal processing with high-level accuracy. The research method for designing the simulation codes for observing the EMG signal modeling and digital filtering techniques with mathematical approaches from the signals and systems concepts. The results confirm that the outcomes of this study met the performance target for noise removal techniques of EMG signals in real-world applications.
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Staudenmann, D., Roeleveld, K., Stegeman, D. F., and Dieën, J. H. V., “Methodological aspects of SEMG recordings for force estimation – A tutorial and review,” J. Electromyogr. Kines., vol. 20, pp. 375-387, 2010. DOI: https://doi.org/10.1016/j.jelekin.2009.08.005
Balestra, G., Frassinelli, S., Knaflitz, M., and Molinari, F., “Time-frequency analysis of surface myoelectric signals during athletic movement,” IEEE Eng. Med. Biol. Mag., vol. 20, pp. 106-115, 2001. DOI: https://doi.org/10.1109/51.982282
Phinyomark, A., Phukapattranont, P., and Limsakul, C., “Feature reduction and selection for EMG signal classification,” Expert Syst. Appl., vol. 39, pp. 7420-7431, Jun. 2012. DOI: https://doi.org/10.1016/j.eswa.2012.01.102
Abbaspour, S., Fallah, A., Linden, M., and Gholamhosseini, H., “A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet,” J. Electromyogr. Kines., vol. 26, pp. 52-59, 2016. DOI: https://doi.org/10.1016/j.jelekin.2015.11.003
Zhou, P., “Eliminating cardiac contamination from myoelectric control signals developed by targeted muscle reinnervation,” Physiol Meas., vol. 27, pp. 1311-1327, 2007. DOI: https://doi.org/10.1088/0967-3334/27/12/005
Oo, T., and Phukpattaranont, P., “Accounting for SNR in an Algorithm using Wavelet Transform to Remove ECG Interference from EMG Signals”, Fluctuation and Noise Letters, Accepted for publication.
Gronfors, T., Sihvonen, T., Lamsa, V., and Paivinen, N., “Spectral Factors and Medical Parameters of EMG Signals,” IET 3rd International Conference On Advances in Medical, Signal and Information Processing - MEDSIP, pp. 1–4, 2006. DOI: https://doi.org/10.1049/cp:20060345
Tun, H. M., "Photoplethysmography (PPG) Scheming System Based on Finite Impulse Response (FIR) Filter Design in Biomedical Applications," International Journal of Electrical and Electronic Engineering & Telecommunications, vol. 10(4), pp. 272-282, July 2021. https://doi.org/10.18178/ijeetc.10.4.272-282. DOI: https://doi.org/10.18178/ijeetc.10.4.272-282
Zar, W. T., and Tun, H. M., “Non-contact Heartbeat Detection Using Viterbi Algorithm Based on Distribution of Difference of Two-Adjacent R-R Intervals”. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829, 2022. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_63. DOI: https://doi.org/10.1007/978-981-16-8129-5_63
Wah, T. N., Swe, H. M. T., and Tun, H. M., “Implementation of Instrumentation Amplifier Design for Biomedical Engineering Applications,” Solid State Technology, 64(2), pp. 2891-2897, 2021.
Tun, H.M., Naing, Z.M., Moe, W.K. and Latt, M.M., “Analysis of heart rate variability based on a quantitative approach,” MOJ Proteomics Bioinform, 7(2), pp.131-141, 2018. DOI: https://doi.org/10.15406/mojpb.2018.07.00223
Tun, H. M., Moe, W. K., and Naing, Z. M., “Analysis of Computer-Aided Identification System for ECG Characteristic Points,” International Journal of Biomedical Science and Engineering, vol. 3(4), pp. 49-61, 2015. https://doi.org/10.11648/j.ijbse.20150304.11. DOI: https://doi.org/10.11648/j.ijbse.20150304.11
Tun, H. M., Moe, W. K., and Naing, Z. M., “Analysis on ECG Data Compression Using Wavelet Transform Technique. International Journal of Psychological and Brain Sciences. vol. 2(6), pp. 127-140, 2017. https://doi.org/10.11648/j.ijpbs.20170206.12. DOI: https://doi.org/10.11648/j.ijpbs.20170206.12
Aung, P. T. T., Khaing, A. S., and Tun, H. M., “MR brain image segmentation using region-based active contour model,” International Journal of Scientific & Technology Research, 4(8), pp.92-97, 2015.
Myint, W. W., Tun, K. S., and Tun, H. M., “Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images,” Machine Learning Research, vol. 3(3), pp. 49-59, 2018. https://doi.org/10.11648/j.mlr.20180303.11. DOI: https://doi.org/10.11648/j.mlr.20180303.11
Tun, H. M., Wulansari, R. E., Pradhan, D., and Naing, Z. M., “Design, fabrication and measurement of metal-semiconductor field effect transistor based on zinc oxide material,” Journal of Engineering Researcher and Lecturer, vol 2(3), pp. 104-111, 2023. DOI: https://doi.org/10.58712/jerel.v2i3.103
Tun, H. M., Pradhan, D., Behera, M., New, M. S., Aye, S.M., Mon, A. T., and Naing, Z. M., “Development of Square Patch Microstrip Antenna Design by Using Three Dimension Finite Difference Time Domain Methods,” Journal of Novel Engineering Science and Technology, vol 2(3), pp. 83-88, 2023. DOI: https://doi.org/10.56741/jnest.v2i03.412
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