Analysis of induced dynamic biceps EMG signal complexity using Markov transition networks

Merletti R, Farina D. Surface electromyography: physiology, engineering, and applications. New York: John Wiley & Sons; 2016.

Book  Google Scholar 

Vijayvargiya A, Singh B, Kumar R, Tavares JMRS. Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview. Biomed Eng Lett. 2022;12(4):343–58.

Article  Google Scholar 

Makaram N, Karthick PA, Gopinath V, Swaminathan R. Surface electromyography-based muscle fatigue analysis using binary and weighted visibility graph features. Fluct Noise Lett. 2020. https://doi.org/10.1142/S0219477521500164.

Article  Google Scholar 

Yadav D, Veer K. Recent trends and challenges of surface electromyography in prosthetic applications. Biomed Eng Lett. 2023;13:353–73. https://doi.org/10.1007/s13534-023-00281-z.

Article  Google Scholar 

Beretta-Piccoli M, Cescon C, D’Antona G. Evaluation of performance fatigability through surface EMG in health and muscle disease: state of the art. Arab J Basic Appl Sci. 2021;28(1):20–40.

Google Scholar 

Besomi M, et al. Consensus for experimental design in electromyography (CEDE) project: amplitude normalization matrix. J Electromyogr Kinesiol. 2020;53:102438.

Article  Google Scholar 

Moissenet F, Tabard-Fougère A, Genevay S, Armand S. Normalisation of a biarticular muscle EMG signal using a submaximal voluntary contraction: choice of the standardised isometric task for the rectus femoris, a pilot study. Gait Posture. 2022;91:161–4.

Article  Google Scholar 

Burden A. How should we normalize electromyograms obtained from healthy participants? What we have learned from over 25 years of research. J Electromyogr Kinesiol. 2010;20(6):1023–35.

Article  Google Scholar 

Makaram N, Karthick PA, Swaminathan R. Analysis of dynamics of EMG signal variations in fatiguing contractions of muscles using transition network approach. IEEE Trans Instrum Meas. 2021;70:1–8.

Article  Google Scholar 

Rampichini S, Vieira TM, Castiglioni P, Merati G. Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: a review. Entropy. 2020. https://doi.org/10.3390/E22050529.

Article  Google Scholar 

Bonato P, Heng MSS, Gonzalez-Cueto J, Leardini A, O’Connor J, Roy SH. EMG-based measures of fatigue during a repetitive squat exercise. IEEE Eng Med Biol Mag. 2001;20(6):133–43.

Article  Google Scholar 

Liao F, Zhang X, Cao C, Hung IYJ, Chen Y, Jan YK. Effects of muscle fatigue and recovery on complexity of surface electromyography of biceps brachii. Entropy. 2021;23(8):1036. https://doi.org/10.3390/e23081036.

Article  Google Scholar 

Cifrek M, Medved V, Tonković S, Ostojić S. Surface EMG based muscle fatigue evaluation in biomechanics. Clin Biomech. 2009;24(4):327–40. https://doi.org/10.1016/j.clinbiomech.2009.01.010.

Article  Google Scholar 

Venugopal G, Navaneethakrishna M, Ramakrishnan S. Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals. Expert Syst Appl. 2014;41(6):2652–9. https://doi.org/10.1016/j.eswa.2013.11.009.

Article  Google Scholar 

Silva VF, Silva ME, Ribeiro P, Silva F. “Time series analysis via network science: concepts and algorithms. Wiley Interdiscip Rev Data Min Knowl Discov. 2021;11(3):e1404.

Article  Google Scholar 

Zou Y, Donner RV, Marwan N, Donges JF, Kurths J. Complex network approaches to nonlinear time series analysis. Phys Rep. 2019;787:1–97.

Article  MathSciNet  Google Scholar 

Sakellariou K, Stemler T, Small M. Markov modeling via ordinal partitions: an alternative paradigm for network-based time-series analysis. Phys Rev E. 2019;100(6):62307.

Article  Google Scholar 

Sasidharan D, Gopinath V, Swaminathan R. A proposal to analyze muscle dynamics under fatiguing contractions using surface Electromyography signals and fuzzy recurrence network features. Fluct Noise Lett. 2023;22(5):2350016–33.

Article  Google Scholar 

Sasidharan D, Venugopal G, Swaminathan R. Complexity Analysis of surface electromyography signals under fatigue using Hjorth parameters and bubble entropy. J. Mech. Med. Biol., p. 2340051, 2023.

Bugueño M, Molina G, Mena F, Olivares P, Araya M. Harnessing the power of CNNs for unevenly-sampled light-curves using Markov transition field. Astron Comput. 2021;35:100461.

Article  Google Scholar 

Khalifa Y, Mandic D, Sejdić E. A review of hidden Markov models and recurrent neural networks for event detection and localization in biomedical signals. Inf Fusion, 2020.

Li R, Wu Y, Wu Q, Dey N, Crespo RG, Shi F. Emotion stimuli-based surface electromyography signal classification employing Markov transition field and deep neural networks. Measurement. 2022;189:110470.

Article  Google Scholar 

Qiu JL, Zhao WY. Data encoding visualization based cognitive emotion recognition with AC-GAN applied for denoising. In: Proc. 2018 IEEE 17th Int Conf Cogn Informatics Cogn Comput ICCI*CC 2018, pp. 222–227, 2018, doi: https://doi.org/10.1109/ICCI-CC.2018.8482097.

Sasidharan D, Venugopal G, Ramakrishnan S. Muscle fatigue analysis by visualization of dynamic surface EMG signals using Markov transition field. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2022, pp. 3611–3614.

Landin D, Thompson M, Jackson MR. Actions of the biceps brachii at the shoulder: a review. J Clin Med Res. 2017;9(8):667.

Article  Google Scholar 

Johnson M, Polgar J, Weightman D, Appleton D. Data on the distribution of fibre types in thirty-six human muscles: an autopsy study. J Neurol Sci. 1973;18(1):111–29.

Article  Google Scholar 

Kaplanis PA, Pattichis CS, Hadjileontiadis LJ, Roberts VC. Surface EMG analysis on normal subjects based on isometric voluntary contraction. J Electromyogr Kinesiol. 2009;19(1):157–71.

Article  Google Scholar 

Hari LM, Venugopal G, Ramakrishnan S. Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features. Proc Inst Mech Eng Part H J Eng Med, p 09544119211048011, 2021

Venugopal G, Ramakrishnan S. Analysis of progressive changes associated with muscle fatigue in dynamic contraction of biceps brachii muscle using surface EMG signals and bispectrum features. Biomed Eng Lett. 2014;4(3):269–76.

Article  Google Scholar 

Makara N, Swaminathan R. Characterizing the dynamics of surface electromyography signals in muscle fatigue through visibility motif networks. IEEE Sensors Lett 2023.

Liu L, Wang Z. Encoding temporal Markov dynamics in graph for visualizing and mining time series. arXiv Prepr. arXiv1610.07273, 2016.

Supriya S, Siuly S, Wang H, Cao J, Zhang Y. Weighted visibility graph with complex network features in the detection of epilepsy. IEEE access. 2016;4:6554–66.

Article  Google Scholar 

Karthick PA, Ghosh DM, Ramakrishnan S. Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms. Comput Methods Programs Biomed. 2018;154:45–56. https://doi.org/10.1016/j.cmpb.2017.10.024.

Article  Google Scholar 

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