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Nonlinear surface EMG analysis to detect the neuroprotective effect of citicoline in rat sciatic nerve crush injury

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Abstract

Surface electromyography (sEMG) is a method involving physiological signals with a complex behavior. The aim is to analyze the sEMG signals by nonlinear techniques for investigating the possible neuroprotective effect of citicoline for early period of administration in rat sciatic nerve crush injury. Thirty-two Wistar rats were randomized into four groups: the sham-operated group with the intact sciatic nerve and the sciatic nerve crush groups, which received crush on the left sciatic nerve and administrated i.p. citicoline (50 and 250 mg/kg/day, 7 day) or saline (control group). Function assessment analysis was performed and sEMG signals were recorded and analyzed with nonlinear methods. Citicoline administration improved functional recovery in comparison with control group. Largest Lyapunov exponent and correlation dimension parameters were decreased due to the crush injury and increased related with the healing of sciatic nerve. Results of nonlinear analysis of sEMG are in line with the results of functional recovery and electrophysiological assessments. These results suggest that administration of citicoline protects the sciatic nerve from the crush injury which may be attributed to its antioxidative properties. Nonlinear analysis of sEMG is a promising supporting method for determining the nerve regeneration process during the treatment of peripheral nerve injuries.

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References

  1. Padmanabhan P, Puthusserypady S (2009) Nonlinear analysis of EMG signals - a chaotic approach. Conf Proc IEEE Eng Med Biol Soc 2006:608–611. https://doi.org/10.1109/IEMBS.2004.1403231

    Article  Google Scholar 

  2. Aziz S, Khan MU, Aamir F et al (2019) Electromyography (EMG) data-driven load classification using empirical mode decomposition and feature analysis. 2019 Int Conf Front Inf Technol (FIT) 272–2725. https://doi.org/10.1109/FIT47737.2019.00058.

  3. Hudgins B, Parker P, Scott R (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40:82–94

    Article  CAS  PubMed  Google Scholar 

  4. Farina D, Merletti R, Nazzaro M et al (2001) Effect of joint angle on EMG variables in leg and thigh muscles. IEEE Eng Med Biol Mag 20(6):62–71. https://doi.org/10.1109/51.982277

  5. Englehart K, Hudgin B, Parker P (2001) A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans Biolmed Eng 48:302–311

    Article  CAS  Google Scholar 

  6. Hong T, Zhang X, Ma H et al (2016) Fatiguing effects on the multi-scale entropy of surface electromyography in children with cerebral palsy. Entropy 18(5):177. https://doi.org/10.3390/e18050177

    Article  Google Scholar 

  7. Talebinejad M, Chan AD, Miri A et al (2009) Fractal analysis of surface electromyography signals: a novel power spectrum-based method. J Electromyogr Kinesiol 19(5):840–50. https://doi.org/10.1016/j.jelekin.2008.05.004

    Article  PubMed  Google Scholar 

  8. Bauder AR, Ferguson TA (2012) Reproduceable mouse sciatic nerve crush and subsequent assessment of regeneration by whole mount muscle analysis. J Vis Exp 60:e3606. https://doi.org/10.3791/3606

    Article  Google Scholar 

  9. Weiss GB (1995) Metabolism and actions of CDP-choline as an endogenously as citicoline. Life Sci 56:637–660

    Article  CAS  PubMed  Google Scholar 

  10. Özay R, Bekar A, Kocaeli H et al (2007) Citicoline improves functional recovery, promotes nerve regeneration, and reduces postoperative scaling after peripheral nerve surgery in rats. Surg Neurol 68:615–622

    Article  PubMed  Google Scholar 

  11. Aslan E, Kocaeli H, Bekar A et al (2011) CDP-choline and its endogenous metabolites, cytidine and choline, promote the nerve regeneration and improve the functional recovery of injured rat sciatic nerves. Neurol Res 33(7):766–773

    Article  CAS  PubMed  Google Scholar 

  12. Caner B, Kafa MI, Bekar A et al (2012) Intraperitoneal administration of CDP-choline or a combination of cytidine plus choline improves nerve regeneration and functional recovery in a rat model of sciatic nerve injury. Neurol Res 34(3):238–245

    Article  CAS  PubMed  Google Scholar 

  13. Kaplan T, Kafa IM, Cansev M et al (2014) Investigation of the dose-dependency of citicoline effects on nerve regeneration and functional recovery in a rat model of sciatic nerve injury. Turk Neurosurg 24(1):54–62

    PubMed  Google Scholar 

  14. Varejão AS, Melo-Pinto P, Meek MF et al (2004) Methods for the experimental functional assessment of rat sciatic nerve regeneration. Neurol Res 26:186–194

    Article  PubMed  Google Scholar 

  15. Lu´ıs AL, Amado S, Geunad S et al (2007) Long-term functional and morphological assessment of a standardized rat sciatic nerve crush injury with a non-serrated clamp. J Neurosci Methods 163:92–104

    Article  Google Scholar 

  16. De Luca CJ, Gilmore LD, Kuznetsov M et al (2010) Filtering the surface EMG signal: movement artifact and baseline noise contamination. J Biomech 43(8):1573–1579. https://doi.org/10.1016/j.jbiomech.2010.01.027

    Article  PubMed  Google Scholar 

  17. Nodes T, Gallagher N (1982) Median filters: Some modifications and their properties. IEEE Trans Acoust 30(5):739–746. https://doi.org/10.1109/TASSP.1982.1163951

    Article  Google Scholar 

  18. Chowdhury RH, Reaz MB, Ali MA et al (2013) Surface electromyography signal processing and classification techniques. Sensors (Basel) 13(9):12431–66. https://doi.org/10.3390/s130912431

    Article  Google Scholar 

  19. Andrade AO, Nasuto S, Kyberd P et al (2006) EMG signal filtering based on empirical mode decomposition. Biomed Signal Process Control 1(1):44–55

    Article  Google Scholar 

  20. Baspinar U, Senyurek VY, Dogan B et al (2015) A comparative study of denoising sEMG signals. Turk J Elec Eng Comp Sci 23:931–944

    Article  Google Scholar 

  21. Flanders M (2002) Choosing a wavelet for single-trial EMG. J Neurosci Methods 116:165–177

    Article  PubMed  Google Scholar 

  22. Rafiee J, Rafiee MA, Prause N et al (2011) Wavelet basis functions in biomedical signal processing. Expert Syst Appl 38(5):6190–6201

    Article  Google Scholar 

  23. Hussain MS, Reaz MBI, Mohd-Yasin F et al (2009) Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction. Expert Syst 26(1):35–48

    Article  Google Scholar 

  24. Hu Z, Wang Z (2004) Detecting the motor unit action potential from surface EMG signals based on wavelet transform. IEEE Biomed Circuits Syst 6-15.https://doi.org/10.1109/BIOCAS.2004.1454166

  25. Hu X, Wang Z, Ren X (2005) Classification of surface EMG signal using relative wavelet packet energy. Comput Methods Programs Biomed 79(3):189–195

    Article  PubMed  Google Scholar 

  26. Huang Y, Chen K, Zhang X et al (2021) Motion estimation of elbow joint from sEMG using continuous wavelet transform and back propagation neural networks. Biomed Signal Process Control 68:102657

    Article  Google Scholar 

  27. Sobahi NM (2011) Denoising of EMG signals based on wavelet transform. Asia Trans Eng 1(5):17–23

    Google Scholar 

  28. Jiang CF, Kuo SL (2007) A comperative study of wavelet denoising of surface electromyographic signals. Annu Int Conf IEEE Eng Med Biol Soc 2007:1868–1871

    PubMed  Google Scholar 

  29. He C, Xing J, Li J et al (2015) A new wavelet threshold determination method considering interscale correlation in signal denoising. Math Probl Eng 2015:280251

    Google Scholar 

  30. Priya KD, Rao GS, Rao PSVS (2016) Comparative analysis of wavelet thresholding techniques with wavelet-wiener filter on ECG signal. Procedia Comput Sci 87:178–183

    Article  Google Scholar 

  31. Chen G, Xie W, Zhao Y (2013) Wavelet-based denoising: a brief review. 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), 570–574

  32. Walker JS (1999) A premier on wavelets and their scientific applications. Chapman and Hall/CRC, London

    Google Scholar 

  33. Diab A, Falou O, Hassan M et al (2015) Effect of filtering on the classification rate of nonlinear analysis methods applied to uterine EMG signals. Annu Int Conf IEEE Eng Med Biol Soc 2015:4182–4185. https://doi.org/10.1109/EMBC.2015.7319316

    Article  PubMed  Google Scholar 

  34. Takens F (1981) Detecting strange attractors in turbulence. Lect Notes Math 898:366

    Article  Google Scholar 

  35. Fraser AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33(2):1134–1140

    Article  CAS  Google Scholar 

  36. Cao L (1997) Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110(1):43–50

    Article  Google Scholar 

  37. Abarbanel HDI (1993) The analysis of observed chaotic data in physical systems. Rev Mod Phys 65(4):1331–1392

    Article  Google Scholar 

  38. Rosenstein MT, Collins JJ, De Luca CJ (1993) A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65:117–134

    Article  Google Scholar 

  39. Grassberger P, Procaccia I (1983) Characterization of strange attractors. Phys Rev Lett 50(5):346–349

    Article  Google Scholar 

  40. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88:2297–2301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Correa PR, Catai AM, Takakura IT et al (2010) Heart rate variability and pulmonary infections after myocardial revascularization. Arq Bras Cardiol 95:448–456

    Article  PubMed  Google Scholar 

  42. Hurtado O, Lizasoain L, Moro MA (2011) Neuroprotection and recovery: recent data at the bench on citicoline. Stroke 42:S33–S35

    Article  CAS  PubMed  Google Scholar 

  43. Cetinkaya M, Cansev M, Kafa IM et al (2013) Cytidine 5’-diphosphocholine ameliorates hyperoxic lung injury in a neonatal rat model. Pediatr Res 74(1):26–33. https://doi.org/10.1038/pr.2013.68

    Article  CAS  PubMed  Google Scholar 

  44. Sobrado M, Lopez MG, Carceller F et al (2003) Combined nimodipine and citicoline reduce infarct size, attenuate apoptosis and increase Bcl-2 expression after focal cerebral ischemia. Neuroscience 118:107–113

    Article  CAS  PubMed  Google Scholar 

  45. Adibhatla RM, Hatcher JF (2002) Citicoline mechanisms and clinical efficacy in cerebral ischemia. J Neurosci Res 70:133–139

    Article  CAS  PubMed  Google Scholar 

  46. Alberghina M, Viola M, Serra I et al (1981) Effect of CDP-choline on the biosynthesis of phospholipids in brain regions during hypoxic treatment. J Neurosci Res 6:421–433

    Article  CAS  PubMed  Google Scholar 

  47. Grieb P, Rejdak R (2002) Pharmacodynamics of citicoline relevant to the treatment of glaucoma. J Neurosci Res 67:143–148

    Article  CAS  PubMed  Google Scholar 

  48. Yücel N, Cayli SR, Ateş O et al (2006) Evaluation of the neuroprotective effects of citicoline after experimental spinal cord injury: improved behavioral and neuroanatomical recovery. Neurochem Res 31:767–775

    Article  PubMed  CAS  Google Scholar 

  49. Araujo-Filho HG, Quintans-Junior LJ, Barreto AS et al (2016) Neuroprotective effect of natural products on peripheral nerve degeneration: a systematic review. Neurochem Res 41:647–658

    Article  CAS  PubMed  Google Scholar 

  50. Sun H, Liu J, Ding F et al (2006) Investigation of differentially expressed proteins in rat gastrocnemius muscle during denervation-reinnervation. J Muscle Res Cell Motil 27:241–250

    Article  CAS  PubMed  Google Scholar 

  51. Wang Y, Wang H, Mi D et al (2015) Periodical assessment of electrophysiological recovery following sciatic nerve crush via surface stimulation in rats. Neurol Sci 36:449–456

    Article  PubMed  Google Scholar 

  52. Leal-Cardoso JH, Matos-Brito BG, Lopes-Junior JEG et al (2004) Effects of estragole on the compound action potential of the rat sciatic nerve. Braz J Med Biol Res 37:1193–1198

    Article  CAS  PubMed  Google Scholar 

  53. Lin H, Wang H, Chen D et al (2007) A dose-effect relationship of ginkgo biloba extract to nerve regeneration in a rat model. Microsurgery 27:673–677

    Article  PubMed  Google Scholar 

  54. Acharya UR, Faust O, Sreec V et al (2014) Linear and nonlinear analysis of normal and CAD-affected heart rate signals. Comput Methods Programs Biomed 113:55–68

    Article  PubMed  Google Scholar 

  55. Chen X, Xu Y, Tang Y et al (2013) Nonlinear dynamics of electroencephalography study in schizophrenic patients. Chin Med J 126(15):2886–2889

    PubMed  Google Scholar 

  56. Caliskan SG, Bilgin MD, Polatli M (2018) Nonlinear analysis of electrodermal activity signals for healthy subjects and patients with chronic obstructive pulmonary disease. Australas Phys Eng Sci Med 41(2):487–494. https://doi.org/10.1007/s13246-018-0649-4

    Article  PubMed  Google Scholar 

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Funding

This work was supported by a grant from the Adnan Menderes University, Aydin, Turkey, through grant no. TPF-07016.

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Authors and Affiliations

  1. Department of Physics, Sciences and Arts Faculty, Aydın Adnan Menderes University, Merkez Kampus, Aytepe, Aydin, 09010, Turkey

    Serife G. Çalışkan

  2. Department of Biophysics, School of Medicine, Aydın Adnan Menderes University, Aydin, 09010, Turkey

    Mehmet D. Bilgin

Authors
  1. Serife G. Çalışkan
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Contributions

Serife Gokce Caliskan: Conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, project administration, visualization, and writing—review and editing. Mehmet Dincer Bilgin: Conceptualization, methodology, investigation, validation, resources, supervision, funding acquisition, project administration, and writing—review and editing.

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Correspondence to Serife G. Çalışkan.

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All procedures performed in studies involving animals were in accordance with the ethical standards of Adnan Menderes University’s Animal Experimentation Ethics Committee (Protocol number: 2010/019).

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Çalışkan, S.G., Bilgin, M.D. Nonlinear surface EMG analysis to detect the neuroprotective effect of citicoline in rat sciatic nerve crush injury. Med Biol Eng Comput 60, 2865–2875 (2022). https://doi.org/10.1007/s11517-022-02639-4

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  • DOI: https://doi.org/10.1007/s11517-022-02639-4

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