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The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia

  • Luis A. Guerra
  • Laura C. Lanzarini
  • Luis E. Sánchez
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 94)

Abstract

Dementia being a syndrome caused by a brain disease of a chronic or progressive nature, in which the irreversible loss of intellectual abilities, learning, expressions arises; including memory, thinking, orientation, understanding and adequate communication, of organizing daily life and of leading a family, work and autonomous social life; leads to a state of total dependence; therefore, its early detection and classification is of vital importance in order to serve as clinical support for physicians in the personalization of treatment programs. The use of the electroencephalogram as a tool for obtaining information on the detection of changes in brain activities. This article reviews the types of cognitive spectrum dementia, biomarkers for the detection of dementia, analysis of mental states based on electromagnetic oscillations, signal processing given by the electroencephalogram, review of processing techniques, results obtained where it is proposed the mathematical model about neural networks, discussion and finally the conclusions.

Keywords

Biomarker Dementia Electroencephalogram Signal processing Neuronal network 

References

  1. 1.
    Griffa, A.: Structural Connectomics in Brain Diseases. Neuroimage. 80, 515–526 (2013)CrossRefGoogle Scholar
  2. 2.
    Sporns, O., Tononi, G., Kötter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1(4), e42 (2005)CrossRefGoogle Scholar
  3. 3.
    Al-Qazzaz, N.K.: Role of EEG as biomarker in the early detection and classification of dementia. Sci. World J. 2014, 16 (2014)CrossRefGoogle Scholar
  4. 4.
    Cedazo-Minguez, A., Winblad, B.: Biomarkers for Alzheimer’s disease and other forms of dementia: clinical needs, limitations and future aspects. Exp. Gerontol. 45(1), 5–14 (2010)CrossRefGoogle Scholar
  5. 5.
    Hampel, H.: Biomarkers for Alzheimer’s Disease: academic, industry and regulatory perspectives. Nat. Rev. Drug Discov. 9(7), 560–574 (2010)CrossRefGoogle Scholar
  6. 6.
    Vialatte, F.B.: Improving the specificity of EEG for diagnosing Alzheimer’s Disease. Int. J. Alzheimer’s Dis. 2011, 7 (2011)Google Scholar
  7. 7.
    Hampel, H.: Perspective on future role of biological markers in clinical therapy trials of Alzheimer’s disease: a long-range point of view beyond 2020. Biochem. Pharmacol. 88(4), 426–449 (2014)CrossRefGoogle Scholar
  8. 8.
    Borson, S.: Improving dementia care: the role of screening and detection of cognitive impairment. Alzheimer’s Dement. 9(2), 151–159 (2013)CrossRefGoogle Scholar
  9. 9.
    DeKosky, S.T., Marek, K.: Looking backward to move forward: early detection of neurodegenerative disorders. Science 302(5646), 830–834 (2003)CrossRefGoogle Scholar
  10. 10.
    Román, G.C.: Vascular dementia may be the most common form of dementia in the elderly. J. Neurol. Sci. 203, 7–10 (2002)CrossRefGoogle Scholar
  11. 11.
    Thal, D.R., Grinberg, L.T., Attems, J.: Vascular dementia: different forms of vessel disorders contribute to the development of dementia in the elderly brain. Exp. Gerontol. 47(11), 816–824 (2012)CrossRefGoogle Scholar
  12. 12.
    Petersen, R.C.: Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256(3), 183–194 (2004)CrossRefGoogle Scholar
  13. 13.
    Dorval, V., Nelson, P.T., Hébert, S.S.: Circulating MicroRNAs in Alzheimer’s Disease: The Search for Novel Biomarkers. Frontiers in Molecular Neuroscience 6, 24 (2013)Google Scholar
  14. 14.
    Poil, S.S.: Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage. Front. Aging Neurosci. 5, 58 (2013)CrossRefGoogle Scholar
  15. 15.
    Mattsson, N.: CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302(4), 385–393 (2009)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Paraskevas, G.: CSF biomarker profile and diagnostic value in vascular dementia. Eur. J. Neurol. 16(2), 205–211 (2009)CrossRefGoogle Scholar
  17. 17.
    Frankfort, S.V.: Amyloid beta protein and tau in cerebrospinal fluid and plasma as biomarkers for dementia: a review of recent literature. Curr. Clin. Pharmacol. 3(2), 123–131 (2008)CrossRefGoogle Scholar
  18. 18.
    Folin, M.: Apolipoprotein E as vascular risk factor in neurodegenerative dementia. Int. J. Mol. Med. 14, 609–614 (2004)Google Scholar
  19. 19.
    Schneider, A.L., Jordan, K.G.: Regional attenuation without delta (RAWOD): a disqtinctive EEG pattern that can aid in the diagnosis and management of severe acute ischemic stroke. Am. J. Electroneurodiagn. Technol. 45(2), 102–117 (2005)Google Scholar
  20. 20.
    Henderson, G.: Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Trans. Biomed. Eng. 53(8), 1557–1568 (2006)CrossRefGoogle Scholar
  21. 21.
    Zhao, P., Ifeachor, E.: EEG assessment of Alzheimers diseases using universal compression algorithm. In: Proceedings of the 3rd International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2007), Plymouth, UK, 25 July 2007Google Scholar
  22. 22.
    Ochoa, J.B.: EEG signal classification for brain computer interface applications. Ec. Polytech. Federale de Lausanne 7, 1–72 (2002)Google Scholar
  23. 23.
    Guérit, J.: EEG and evoked potentials in the intensive care unit. Neurophysiol. Clin. Clin. Neurophysiol. 29(4), 301–317 (1999)CrossRefGoogle Scholar
  24. 24.
    Moretti, D.: Quantitative EEG markers in mild cognitive impairment: degenerative versus vascular brain impairment. Int. J. Alzheimer’s Dis. 2012, 12 (2012)Google Scholar
  25. 25.
    Moretti, D.: Vascular damage and EEG markers in subjects with mild cognitive impairment. Clin. Neurophysiol. 118(8), 1866–1876 (2007)CrossRefGoogle Scholar
  26. 26.
    Pizzagalli, D.A.: Electroencephalography and high-density electrophysiological source localization. In: Handbook of Psychophysiology, vol. 3, pp. 56–84 (2007)Google Scholar
  27. 27.
    John, E.: Developmental equations for the electroencephalogram. Science 210(4475), 1255–1258 (1980)CrossRefGoogle Scholar
  28. 28.
    Jeong, J.: EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol. 115(7), 1490–1505 (2004)CrossRefGoogle Scholar
  29. 29.
    Taywade, S., Raut, R.: A review: EEG signal analysis with different methodologies. In: Proceedings of the National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012) (2014)Google Scholar
  30. 30.
    Husain, A., Tatum, W., Kaplan, P.: Handbook of EEG Interpretation. Demos Medical, New York (2008)Google Scholar
  31. 31.
    Punapung, A., Tretriluxana, S., Chitsakul, K.: A design of configurable ECG recorder module. In: Biomedical Engineering International Conference (BMEiCON). IEEE (2012)Google Scholar
  32. 32.
    Klem, G.H.: The Ten-Twenty Electrode System of the International FederationGoogle Scholar
  33. 33.
    Anderson, C.W., Sijercic, Z.: Classification of EEG signals from four subjects during five mental tasks. In: Solving Engineering Problems with Neural Networks: Proceedings of the Conference on Engineering Applications in Neural Networks (EANN 1996), Turkey (1996)Google Scholar
  34. 34.
    Müller, T.: Selecting relevant electrode positions for classification tasks based on the electro-encephalogram. Med. Biol. Eng. Compu. 38(1), 62–67 (2000)CrossRefGoogle Scholar
  35. 35.
    Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley, Chichester (2013)Google Scholar
  36. 36.
    Moretti, D.V.: Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clin. Neurophysiol. 115(2), 299–308 (2004)CrossRefGoogle Scholar
  37. 37.
    Jung, T.P.: Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin. Neurophysiol. 111(10), 1745–1758 (2000)CrossRefGoogle Scholar
  38. 38.
    Núñez, I.M.B.: EEG Artifact Detection (2011)Google Scholar
  39. 39.
    Guerrero-Mosquera, C., Trigueros, A.M., Navia-Vazquez, A.: EEG Signal Processing for Epilepsy, in Epilepsy-Histological, Electroencephalographic and Psychological Aspects, InTech (2012)Google Scholar
  40. 40.
    Molina, G.N.G.: Direct brain-computer communication through scalp recorded EEG signals. École Polytechnique Fedérale de Lausanne (2004)Google Scholar
  41. 41.
    Naït-Ali, A.: Advanced Biosignal Processing. Springer Science & Business Media, Berlin (2009)CrossRefGoogle Scholar
  42. 42.
    McKeown, M.: A new method for detecting state changes in the EEG: exploratory application to sleep data. J. Sleep Res. 7(S1), 48–56 (1998)CrossRefGoogle Scholar
  43. 43.
    Zikov, T.: A wavelet based denoising technique for ocular artifact correction of the electroencephalogram. In: Proceedings of the Second Joint Engineering in Medicine and Biology, 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference. IEEE (2002)Google Scholar
  44. 44.
    Krishnaveni, V.: Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. J. Neural Eng. 3(4), 338 (2006)CrossRefGoogle Scholar
  45. 45.
    Jahankhani, P., Kodogiannis, V., Revett, K.: EEG signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, JVA 2006. IEEE (2006)Google Scholar
  46. 46.
    Akhtar, M.T., James, C.J.: Focal artifact removal from ongoing EEG–a hybrid approach based on spatially-constrained ICA and wavelet denoising. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009. IEEE (2009)Google Scholar
  47. 47.
    Inuso, G.: Wavelet-ICA methodology for efficient artifact removal from electroencephalographic recordings. In: International Joint Conference on Neural Networks, IJCNN 2007. IEEE (2007)Google Scholar
  48. 48.
    Jelles, B.: Global dynamical analysis of the EEG in Alzheimer’s disease: frequency-specific changes of functional interactions. Clin. Neurophysiol. 119(4), 837–841 (2008)CrossRefGoogle Scholar
  49. 49.
    Escudero, J.: Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings: application to Alzheimer’s disease. Med. Eng. Phys. 31(7), 872–879 (2009)CrossRefGoogle Scholar
  50. 50.
    Hornero, R.: Spectral and nonlinear analyses of MEG background activity in patients with Alzheimer’s disease. IEEE Trans. Biomed. Eng. 55(6), 1658–1665 (2008)CrossRefGoogle Scholar
  51. 51.
    Markand, O.N.: Organic brain syndromes and dementias. Curr. Pract. Clin. Electroencephalogr. 3, 378–404 (1990)Google Scholar
  52. 52.
    Dauwels, J., Vialatte, F., Cichocki, A.: Diagnosis of Alzheimer’s disease from EEG signals: where are we standing? Curr. Alzheimer Res. 7(6), 487–505 (2010)CrossRefGoogle Scholar
  53. 53.
    Jeong, J.: Nonlinear dynamics of EEG in Alzheimer’s disease. Drug Dev. Res. 56(2), 57–66 (2002)MathSciNetCrossRefGoogle Scholar
  54. 54.
    Subha, D.P.: EEG signal analysis: a survey. J. Med. Syst. 34(2), 195–212 (2010)CrossRefGoogle Scholar
  55. 55.
    Abásolo, D.: Analysis of EEG background activity in Alzheimer’s disease patients with lempel-ziv complexity and central tendency measure. Med. Eng. Phys. 28(4), 315–322 (2006)CrossRefGoogle Scholar
  56. 56.
    Escudero, J.: Analysis of electroencephalograms in Alzheimer’s disease patients with multiscale entropy. Physiol. Meas. 27(11), 1091 (2006)CrossRefGoogle Scholar
  57. 57.
    Grassberger, P., Procaccia, I.: Measuring the strangeness of strange attractors. Phys. D 9(1–2), 189–208 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  58. 58.
    Wolf, A.: Determining lyapunov exponents from a time series. Phys. D 16(3), 285–317 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  59. 59.
    Hamadicharef, B.: Performance evaluation and fusion of methods for early detection of Alzheimer disease. In: International Conference on BioMedical Engineering and Informatics, BMEI 2008. IEEE (2008)Google Scholar
  60. 60.
    Henderson, G.T.: Early Detection of Dementia Using The Human Electroencephalogram (2004)Google Scholar
  61. 61.
    Ferenets, R.: Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Trans. Biomed. Eng. 53(6), 1067–1077 (2006)CrossRefGoogle Scholar
  62. 62.
    Costa, M., Goldberger, A.L., Peng, C.-K.: Multiscale entropy analysis of biological signals. Phys. Rev. E 71(2), 021906 (2005)MathSciNetCrossRefGoogle Scholar
  63. 63.
    Subasi, A., Gursoy, M.I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37(12), 8659–8666 (2010)CrossRefGoogle Scholar
  64. 64.
    KavitaMahajan, M., Rajput, M.S.M.: A comparative study of ANN and SVM for EEG classification. Int. J. Eng. Res. Technol. IJERT 1, 1–6 (2012)CrossRefGoogle Scholar
  65. 65.
    Vialatte, F.: Blind source separation and sparse bump modelling of time frequency representation of eeg signals: new tools for early detection of Alzheimer’s disease. In: IEEE Workshop on Machine Learning for Signal Processing. IEEE (2005)Google Scholar
  66. 66.
    Besserve, M.: Classification methods for ongoing EEG and MEG signals. Biol. Res. 40(4), 415–437 (2007)CrossRefGoogle Scholar
  67. 67.
    Garrett, D.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 141–144 (2003)CrossRefGoogle Scholar
  68. 68.
    Lehmann, C.: Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). J. Neurosci. Methods 161(2), 342–350 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Luis A. Guerra
    • 1
  • Laura C. Lanzarini
    • 2
  • Luis E. Sánchez
    • 3
  1. 1.Universidad de las Fuerzas Armadas-ESPESangolquiEcuador
  2. 2.Universidad Nacional de la PlataLa PlataArgentina
  3. 3.Universidad de la ManchaLa ManchaSpain

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