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

Peyman Goli; Elias Mazrooei Rad; Kavian Ghandehari; Mehdi Azarnoosh

doi:doi:10.11648/j.mlr.20170204.15

| Peer-Reviewed

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

Peyman Goli, Elias Mazrooei Rad, Kavian Ghandehari, Mehdi Azarnoosh

Published in Machine Learning Research (Volume 2, Issue 4)

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download

Author Information

Peyman Goli

Electrical and Computer Engineering Department, Khavaran Higher Education Institute, Mashhad, Iran
Elias Mazrooei Rad

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

Specialist of Brain and Neural System, Mashhad, Iran
Mehdi Azarnoosh

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download