Volume 2, Issue 4, December 2017, Page: 148-151
Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods
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
Received: Oct. 23, 2017;       Accepted: Nov. 10, 2017;       Published: Dec. 15, 2017
DOI: 10.11648/j.mlr.20170204.15      View  1534      Downloads  87
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.
Keywords
Mild Alzheimer's Disease, Neural Network, Electroencephalography, Genetic Algorithm
To cite this article
Peyman Goli, Elias Mazrooei Rad, Kavian Ghandehari, Mehdi Azarnoosh, Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods, Machine Learning Research. Vol. 2, No. 4, 2017, pp. 148-151. doi: 10.11648/j.mlr.20170204.15
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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