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Artificial Neuroplasticity with Deep Learning Reconstruction Signals to Reconnect Motion Signals for the Spinal Cord

  • Ricardo Jaramillo Díaz
  • Laura Veronica Jaramillo Marin
  • María Alejandra Barahona GarcíaEmail author
Conference paper
Part of the Advances in Predictive, Preventive and Personalised Medicine book series (APPPM, volume 10)

Abstract

A stroke may be accompanied by consequent disabilities that include neuromuscular, cognitive, somatosensitive, and physiological disconnections. However, neuroplasticity allows the brain to generate new pathways for learning and adapting to external situations after brain injuries, such as stroke. This chapter discusses artificial neuroplasticity based on a deep learning application. Complete electroencephalographic signals are used to reconstruct the original motor signal, restore the necessary pulse, and promote the motion in short-term memory in the spinal cord. The deep learning program was developed using a two-dimensional data process that augments the computed velocity and arrives at a natural procedure. Integrated technology reconstructs the lost signal, restoring motion signals in gray matter through either feature maps of the convolutional neural network of the resulting model or an algorithm that reconstructs the signal through the previously extracted characteristics of artificial neural networks.

Keywords

Stroke Rehabilitation Deep learning Neuroplasticity 

References

  1. 1.
    Caplan LR (2009) Caplan’s stroke a clinical approach, 4th edn. Saunders Elsevier, PhiladelphiaGoogle Scholar
  2. 2.
    Johnson W, Onuma O, Owolabi M, Sachdev S (2016 Sep) Stroke: a global response is needed. Bull World Health Organ 94(9):634–634ACrossRefGoogle Scholar
  3. 3.
    The internet stroke center an independent web source for information about stroke care an research, “Stroke Statistics.” [Online]. Available: http://www.strokecenter.org/patients/about-stroke/stroke-statistics/. Accessed 12 Sep 2018
  4. 4.
    Sieger Silva FA (2016) Boletin cardiecol fase II N° 5, BogotáGoogle Scholar
  5. 5.
    Cramer SC (2018) Treatments to promote neural repair after stroke. J Stroke 20(1):57–70CrossRefGoogle Scholar
  6. 6.
    Longstreth WT, Koepsell TD (2016) Risk factors for Subarachnoid hemorrhage. Stroke 16(3):377–385CrossRefGoogle Scholar
  7. 7.
    Allison RZ, Nakagawa K, Hayashi M, Donovan DJ, Koenig MA (2017 Feb) Derivation of a predictive score for hemorrhagic progression of cerebral contusions in moderate and severe traumatic brain injury. Neurocrit Care 26(1):80–86CrossRefGoogle Scholar
  8. 8.
    Lazaridis C, Smielewski P (2013) Optimal cerebral perfusion pressure: are we ready for it? Neurol Res 35(2):138–149CrossRefGoogle Scholar
  9. 9.
    Kondziella D, Friberg CK (2014) Continuous EEG monitoring in aneurysmal Subarachnoid hemorrhage: a systematic review. Neurocrit Care 22:450–460CrossRefGoogle Scholar
  10. 10.
    Hermann DM, Chopp M (2014) Promoting brain remodelling and plasticity for stroke recovery: therapeutic promise and potential pitfalls of clinical translation. Lancet Neurol 11:369–380CrossRefGoogle Scholar
  11. 11.
    Han K, Chapman SB, Krawczyk DC (2018) Neuroplasticity of cognitive control networks following cognitive training for chronic traumatic brain injury. NeuroImage Clin 18:262–278CrossRefGoogle Scholar
  12. 12.
    Sampaio-Baptista C, Sanders Z-B (2018) Structural plasticity in adulthood with motor learning and stroke rehabilitation. Annu Rev Neurosci 41:25–40CrossRefGoogle Scholar
  13. 13.
    Toosy AT (2018) Valuable insights into visual neuroplasticity after optic neuritis. JAMA Neurol 75(3):274–276CrossRefGoogle Scholar
  14. 14.
    Zhang R (2016) Function of neural stem cells in ischemic brain repair processes. J Cereb Blood Flow Metab 0(0):1–10Google Scholar
  15. 15.
    Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Prog Biomed 161:1–13CrossRefGoogle Scholar
  16. 16.
    Signals and systems basics (2013) In: Signals and systems in biomedical engineering, Springer, Londres, p 40Google Scholar
  17. 17.
    McDonnell MC (2011) The benefits of noise in neural systems: bridging theory and experiment. Nature Neurosci Rev 12(7):415–426CrossRefGoogle Scholar
  18. 18.
    Chong DJ (2007) Introduction to electroencephalography. In: Review of sleep medicine, 2nd edn. Elsevier, Philadelphia, pp 105–141CrossRefGoogle Scholar
  19. 19.
    Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP (2018) Automate d EEG-base d screening of depression using deep convolutional neural network. Comput Methods Prog Biomed 161:103–113CrossRefGoogle Scholar
  20. 20.
    Habibi Aghdam H, Heravi EJ (2017) Guide to convolutional neural networks a practical application to traffic-sign detection and classification. Springer, pp 1–299Google Scholar
  21. 21.
    Wen T, Zhang Z (2018) Deep convolution neural network and autoencoders-based unsupervised feature learning of EEG signals. IEEE ACCESS 6:25399–25410CrossRefGoogle Scholar
  22. 22.
    Ullha I, Hussain M, Qazi E-u-H, Aboalsamh H (2018 April 21) An automated system for epilepsy detection using EEG brain signals based on deep learning approach, vol 107. Elsevier, pp 61–71Google Scholar
  23. 23.
    Acharya UR, Oh SL, Hagiwara Y, Hong Tan J, Adeli H, Subha D (2018 April 17) Automate d EEG-base d screening of depression using deep convolutional neural network. Comput Methods Prog Biomed 161:103–113CrossRefGoogle Scholar
  24. 24.
    Aghdam HH, Heravi EJ (2017) Guide to convolutional neural networks a practical application to traffic-sign detection and classification, Spain, Tarragona. SpringerGoogle Scholar
  25. 25.
    Gollwitzer S, Groemer T (2015) Early prediction of delayed cerebral ischemia in subarachnoid hemorrhage based on quantitative EEG: a prospective study in adults. Clin Neurophysiol 126(8)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ricardo Jaramillo Díaz
    • 1
  • Laura Veronica Jaramillo Marin
    • 1
  • María Alejandra Barahona García
    • 1
    Email author
  1. 1.Universidad ECCIFacultad de Ingeniería BiomédicaBogotáColombia

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