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Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods

Received: 23 October 2017     Accepted: 10 November 2017     Published: 15 December 2017
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Abstract

This research provides a process for diagnosising the mild Alzheimer's disease from the brain signals. Due to the material and spiritual costs of nursing, carring and treatment of this disease, the early acurate diagnosis would be much usedful. Considering the effect of the mild Alzheimer's disease on electroencephalography (EEG), the mild Alzheimer would be diagnosed within the early steps by an appropriate process. First, the brain signals of healthy people and patients are registered for four states: closed–eyes, opened–eyes, recall and stimulation, in three channels Pz, Cz and Fz. Then, optimal features are drawn out by using an Elman neural network and two claaaifiers applying genetic algorithm: linear discriminant analysis (LDA) and Support vector machine (SVM). According to the results of testing phase, among the three channels and four states, Elman neural network is much more efficient for Alziemer diagnosising in Pz channel and the state of irritation in comparison with LDA and SVM in the other channels and states.

Published in Machine Learning Research (Volume 2, Issue 4)
DOI 10.11648/j.mlr.20170204.15
Page(s) 148-151
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Mild Alzheimer's Disease, Neural Network, Electroencephalography, Genetic Algorithm

References
[1] C. A. Briggs, S. Chakroborty, G. E. Stutzmann, "Emerging pathways driving early synaptic pathology in Alzheimer's disease," Biochemical and biophysical research communications, 483(4), 988–97, 2017.
[2] G. Sparacia, K. Sakai, K. Yamada, G. Giordano, R. Coppola, M. Midiri, L. M. Grimaldi, "Assessment of brain core temperature using MR DWI-thermometry in Alzheimer disease patients compared to healthy subjects," Japanese journal of radiology. 35(4), 168–71, 2017.
[3] A. Shimokawa, N. Yatomib, S. Anamizuc, S. Toriid, H. Isonod, Y. Sugaid, and M. Kohnoe, "Influence of deteriorating ability of emotional comprehension on interpersonal behavior in Alzheimer-type dementia," Brain and Cognition 47(3): 423–433, 2001.
[4] G. H. N. Robert M. Chapman, John W. McCrary, John A. Chapmanm, Tiffany C. Sandoval, Maria D. Guillily, Margaret N. Gardner, Lindsey A. Reilly, “Brain event–related potentials: Diagnosing early–stage zheimer’s disease,” vol. 28, pp. 94–201, 2007.
[5] P. D. Tom Meuser, “Clinical Dementia Rating (CDR) Scale,” Alzheimer's Disease Research Center Washington University, vol. 3, pp. 1–4, 2001.
[6] R. E. C. Jeffrey R. Petrella, P. Murali Doraiswamy, “Neuroimaging and Early Diagnosis of Alzheimer Disease: A Look to the Future,” Radiology, vol. 13, pp. 315–336, 2003.
[7] J. M. Gabin, K. Tambs, I. Saltvedt, E. Sund, J. Holmen, Association between blood pressure and Alzheimer disease measured up to 27 years prior to diagnosis: the HUNT Study. Alzheimer's research & therapy, 9(1):37, 2017.
[8] K. Palmer, A. K. Berger, R. Monastero, B. Winblad, L. B ̈ackman, and L. Fratiglioni, "Predictors of progression from mild cognitive impairment to Alzheimer disease," Neurology 68(19): 1596–1602, 2007.
[9] W. M. Weiner, "Imaging and Biomarkers Will be Used for De-tection and Monitoring Progression of Early Alzheimer’s Disease," J. Nutr. Health Aging 4:332, 2009.
[10] T. Mino, H. Saito, J. Takeuchi, K. Ito, A. Takeda, S. Ataka, S. Shiomi, Y. Wada, Y. Watanabe, Y. Itoh, "Cerebral blood flow abnormality in clinically diagnosed Alzheimer's disease patients with or without amyloid β accumulation on positron emission tomography," Neurology and Clinical Neuroscience, 5(2), 55–9, 2017.
[11] P. J. S. Colleen E. Jackson “Electroencephalography and event–related potentials as biomarkers of mild cognitive impairment and mild Alzheimer’s disease,” vol. 23, pp. 137–143, 2008.
[12] S. Y. C. E. H. Park, J. W. Kim, W. W. Whang, H. Tim, “Alzheimer disease detection and analysis using P3 component of ERP in Alzheimer type dementia,” 23rd Annual EMBS International Conference, Turkey, vol. 2, pp. 1–3, 2001.
[13] F. Z. Brill, D. E. Brown, W. N. Martin, “Fast genetic selection of features for neural network classifiers,” IEEE Transactions on Neural Networks, vol. 23, pp. 324–328, 1992.
Cite This Article
  • APA Style

    Peyman Goli, Elias Mazrooei Rad, Kavian Ghandehari, Mehdi Azarnoosh. (2017). Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods. Machine Learning Research, 2(4), 148-151. https://doi.org/10.11648/j.mlr.20170204.15

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    ACS Style

    Peyman Goli; Elias Mazrooei Rad; Kavian Ghandehari; Mehdi Azarnoosh. Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods. Mach. Learn. Res. 2017, 2(4), 148-151. doi: 10.11648/j.mlr.20170204.15

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    AMA Style

    Peyman Goli, Elias Mazrooei Rad, Kavian Ghandehari, Mehdi Azarnoosh. Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods. Mach Learn Res. 2017;2(4):148-151. doi: 10.11648/j.mlr.20170204.15

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  • @article{10.11648/j.mlr.20170204.15,
      author = {Peyman Goli and Elias Mazrooei Rad and Kavian Ghandehari and Mehdi Azarnoosh},
      title = {Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods},
      journal = {Machine Learning Research},
      volume = {2},
      number = {4},
      pages = {148-151},
      doi = {10.11648/j.mlr.20170204.15},
      url = {https://doi.org/10.11648/j.mlr.20170204.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170204.15},
      abstract = {This research provides a process for diagnosising the mild Alzheimer's disease from the brain signals. Due to the material and spiritual costs of nursing, carring and treatment of this disease, the early acurate diagnosis would be much usedful. Considering the effect of the mild Alzheimer's disease on electroencephalography (EEG), the mild Alzheimer would be diagnosed within the early steps by an appropriate process. First, the brain signals of healthy people and patients are registered for four states: closed–eyes, opened–eyes, recall and stimulation, in three channels Pz, Cz and Fz. Then, optimal features are drawn out by using an Elman neural network and two claaaifiers applying genetic algorithm: linear discriminant analysis (LDA) and Support vector machine (SVM). According to the results of testing phase, among the three channels and four states, Elman neural network is much more efficient for Alziemer diagnosising in Pz channel and the state of irritation in comparison with LDA and SVM in the other channels and states.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods
    AU  - Peyman Goli
    AU  - Elias Mazrooei Rad
    AU  - Kavian Ghandehari
    AU  - Mehdi Azarnoosh
    Y1  - 2017/12/15
    PY  - 2017
    N1  - https://doi.org/10.11648/j.mlr.20170204.15
    DO  - 10.11648/j.mlr.20170204.15
    T2  - Machine Learning Research
    JF  - Machine Learning Research
    JO  - Machine Learning Research
    SP  - 148
    EP  - 151
    PB  - Science Publishing Group
    SN  - 2637-5680
    UR  - https://doi.org/10.11648/j.mlr.20170204.15
    AB  - This research provides a process for diagnosising the mild Alzheimer's disease from the brain signals. Due to the material and spiritual costs of nursing, carring and treatment of this disease, the early acurate diagnosis would be much usedful. Considering the effect of the mild Alzheimer's disease on electroencephalography (EEG), the mild Alzheimer would be diagnosed within the early steps by an appropriate process. First, the brain signals of healthy people and patients are registered for four states: closed–eyes, opened–eyes, recall and stimulation, in three channels Pz, Cz and Fz. Then, optimal features are drawn out by using an Elman neural network and two claaaifiers applying genetic algorithm: linear discriminant analysis (LDA) and Support vector machine (SVM). According to the results of testing phase, among the three channels and four states, Elman neural network is much more efficient for Alziemer diagnosising in Pz channel and the state of irritation in comparison with LDA and SVM in the other channels and states.
    VL  - 2
    IS  - 4
    ER  - 

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Author Information
  • Electrical and Computer Engineering Department, Khavaran Higher Education Institute, Mashhad, Iran

  • Electrical and Computer Engineering Department, Khavaran Higher Education Institute, Mashhad, Iran

  • Specialist of Brain and Neural System, Mashhad, Iran

  • Biomedical Engineering Department, Mashhad Branch, Islamic Azad University, Mashhad, Iran

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