Evaluation of Early Detection Methods for Alzheimer's Disease
Bioprocess Engineering
Volume 4, Issue 1, June 2020, Pages: 17-22
Received: Dec. 10, 2019; Accepted: Jan. 13, 2020; Published: Feb. 4, 2020
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Elyas Irankhah, Department of Biomedical Engineering, International University of Imam Reza (AS), Mashhad, Iran
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Amnesia, commonly referred to as Alzheimer’s, is a type of brain dysfunction that gradually dissipates the patient’s mental abilities. Memory disorder usually develops gradually and progresses. At first, memory impairment is limited to recent events and lessons, but old memories are gradually damaged. In this disease, the connection between nerve cells by the formation of neurofibrillary nodes disappeared. Currently, treatment for the disease mainly involves symptomatic treatments, treatment of behavioral disorders and medication use. Although there is no cure for Alzheimer's disease yet, medications can slow the progression of the disease and reduce the severity of memory impairment and behavioral problems. Today, whit the spread of definitive treatment for this disease, in this study, new techniques for the treatment of this disease can be explored by examining the early detection methods of the disease through brain signal processing with classifiers and medical imaging such as MRI and CT Scan. Signal processing has included EEG and ERP brain signals and the use of classifiers such as SVM, LDA and Neural network. In medical image processing, a combination of Neural network and Wavelet is used to expedite the time of diagnosis according to the above method. Given the process under consideration, combining brain signals and medical imaging can provide valuable help in early detection of Alzheimer disease.
Alzheimer's Disease, Image and Signal Processing, Classifiers, Neural Network, Wavelet
To cite this article
Elyas Irankhah, Evaluation of Early Detection Methods for Alzheimer's Disease, Bioprocess Engineering. Vol. 4, No. 1, 2020, pp. 17-22. doi: 10.11648/j.be.20200401.13
Copyright © 2020 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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