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A novel method of early diagnosis of Alzheimer's disease based on EEG signals.

Al-Jumeily, D, Iram, S, Vialatte, FB, Fergus, P and Hussain, A (2015) A novel method of early diagnosis of Alzheimer's disease based on EEG signals. ScientificWorld Journal, 2015. ISSN 2356-6140

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Studies have reported that electroencephalogram signals in Alzheimer's disease patients usually have less synchronization than those of healthy subjects. Changes in electroencephalogram signals start at early stage but, clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques: phase synchrony, magnitude squared coherence, and cross correlation are applied to three different databases of mild Alzheimer's disease patients and healthy subjects. We have compared the right and left temporal lobes of the brain with the rest of the brain areas (frontal, central, and occipital) as temporal regions are relatively the first ones to be affected by Alzheimer's disease. Moreover, electroencephalogram signals are further classified into five different frequency bands (delta, theta, alpha beta, and gamma) because each frequency band has its own physiological significance in terms of signal evaluation. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with Average technique. The simulation results indicated that applying principal component analysis before synchrony measurement techniques shows significantly better results as compared to the lateral one. At the end, all the aforementioned techniques are assessed by a statistical test (Mann-Whitney U test) to compare the results.

Item Type: Article
Uncontrolled Keywords: Alzheimer Disease; Computer Simulation; Electroencephalography; Electroencephalography Phase Synchronization; Humans; Principal Component Analysis; Statistics, Nonparametric; Temporal Lobe
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Computer Science & Mathematics
Publisher: Hindawi Publishing Corporation
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Date Deposited: 08 Aug 2016 08:22
Last Modified: 04 Sep 2021 12:54
DOI or ID number: 10.1155/2015/931387
URI: https://researchonline.ljmu.ac.uk/id/eprint/3654
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