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:: Volume 11, Issue 1 (Winter 2022) ::
Shefaye Khatam 2022, 11(1): 1-12 Back to browse issues page
Separation of Healthy Individuals and Patients with Alzheimer's Disease Using the Effective Communication of Brain Signals
Elias Mazrooei rad * , Hadi Pazhoumand , Shahryar Salmani bajestani
Department of Biomedical Engineering, Khavaran Institute of Higher Education, Mashhad, Iran , elias_mazrooei@yahoo.com
Abstract:   (815 Views)
Introduction: Alzheimer's disease (AD) is a degenerative and progressive disease of the brain that causes the deterioration of intellectual abilities. Approximately 5% of people over 70 years old and 20% of people over 80 years old suffer from this disease. So far, many tools and methods have been provided to diagnose AD. However, in most of these methods, the interactions and connections of different parts of the brain are not considered. Since AD can affect different structures of the brain, damage to any part of the brain disrupts its interaction with other regions. Materials and Methods: The indexes of effective communication between different parts of the brain in two groups of healthy people and subjects with AD were extracted using Granger causality analysis. Following statistical comparisons between the quantitative values of the indexes in different EEG channels, we examined effective communication. Then, we used linear differential analysis to separate the two groups of participants. The data used in the research include EEG signals from 10 healthy subjects and 8 patients with AD (mild and severe). Results: With the correct diagnosis of all patients and only one wrong diagnosis of a healthy subject, an accuracy of 83.33%, an accuracy of 90%, a sensitivity of 100%, and a diagnosis of 80% were obtained for the test data. The effective communication rate of Fz and Cz channels for healthy people is higher than the effective communication of the Pz channel, while for patients with AD, the lowest effective brain communication was observed in the Fz channel, and the highest communication was observed in the Pz channel and sometimes in the Cz channel. Conclusion: The results of Granger features are far better than the results of linear features, despite the fact that the number of extracted linear features was more than Granger features. Therefore, the effectiveness of Granger causality has been proven once again, and it can be said that the interaction indices between EEG channels provided valuable information for classification and led to better identification of patients with AD.
Keywords: Brain, Communication, Alzheimer Disease
Full-Text [PDF 2204 kb]   (1135 Downloads)    
Type of Study: Research --- Open Access, CC-BY-NC | Subject: Neurophysiology
References
1. B.T. Francesco Roselli, Francesco Federico, Vito Lepore, Giovanni Defazio, Paolo Livrea, "Rate of MMSE score change in Alzheimer's disease: Influence of education and vascular risk factors," Clinical Neurology and Neurosurgery, , 2009; vol. 3: pp. 327-30. [DOI:10.1016/j.clineuro.2008.10.006]
2. Prince M, Wimo A, Guerchet M, Ali G, Wu Y, Prina M. World Alzheimer Report 2015. The global impact of dementia. An analysis of prevalence, incidence, cost & trends; Alzheimer's disease International: London.London: Alzheimer's disease International (ADI); 2015.
3. Van Deursen, J. A., E. F. P. M. Vuurman, V. H.J. M. van Kranen-Mastenbroek, F. R. J. Verhey, and W. J. Riedel. "40-Hz steady state response in Alzheimer's disease and mild cognitive impairment." Neurobiology of aging 32, 2011; no. 1: 24-30. [DOI:10.1016/j.neurobiolaging.2009.01.002]
4. Jaeseung Jeong, EEG dynamics in patients with Alzheimer's disease, Invited review, Clinical Neurophysiology, 2004; 115; 1490-1505. [DOI:10.1016/j.clinph.2004.01.001]
5. Jaeseung Jeong, Nonlinear Dynamics of EEG in Alzheimer's disease, Research Overview, DRUG DEVELOPMENT RESEARCH, 2002; 56: 57-66. [DOI:10.1002/ddr.10061]
6. Lee, M.S., Lee, S.H., Moon, E.O., Moon, Y.J., Kim S., Kim, S.H., Jung, I.K.: Neuropsychological correlates of the P300 in patients with Alzheimer's disease. Prog Neuropsychopharmacol Biol Psychiatry, 2013; 40: 62-9. [DOI:10.1016/j.pnpbp.2012.08.009]
7. E. Mazrooei Rad, M. Azarnoosh *, M. Ghoshuni, M.M. Khalilzadeh, Comparison of the function of the Elman neural network and the deep neural network for the diagnosis of mild Alzheimer's disease, shafaye khatam,2021. [DOI:10.52547/shefa.10.1.1]
8. E. J. Burton, R. Barber, E. B. Mukaetova-Ladinska, J. Robson, R. H. Perry, E. Jaros, R. N. Kalaria, and J. T. O'Brien, Medial temporal lobe atrophy on MRI differentiates Alzheimer's disease from dementia with Lewy bodies and vascular cognitive impairment: a prospective study with pathological verification of diagnosis, Brain, 2009; 132: 195-203. [DOI:10.1093/brain/awn298]
9. Hajmanouchehri, R. CT scan and MRI Findings in Patients with Dementia, Iranian Journal of Forensic Medicine, Review Article Ir J Forensic Med, 2017; 23(3): 150-9. [DOI:10.30699/epub.sjfm.23.3.150]
10. Wood PL, Barnette BL, Kaye JA, Quinn JF, Woltjer RL. Non-targeted lipidomics of CSF and frontal cortex grey and white matter in control, mild cognitive impairment, and Alzheimer's disease subjects, Neuropsychopharmacology, 2015; 143-52. [DOI:10.1017/neu.2015.18]
11. Atif Mehmood, Muazzam Maqsood, Muza_ar Bashir and Yang Shuyuan, A Deep Siamese Convolution Neural Network for Multi- Class Classification of Alzheimer Disease; MDPI, 2020; 8:1041-52. [DOI:10.3390/brainsci10020084]
12. Fei- Fei, L.; Deng, J.; Li, K. ImageNet: Constructing a large-scale image database. J. Vis, 2010; 9: 1037-43. [DOI:10.1167/9.8.1037]
13. Baron, J.C.; Chételat, G.; Desgranges, B.; Perchey, G.; Landeau, B.; De La Sayette, V.; Eustache, F. In vivo mapping of gray matter loss with voxel- based morphometry in mild Alzheimer's disease. Neuroimage, 2001;14: 298-309. [DOI:10.1006/nimg.2001.0848]
14. Irfan Ahmad a, Karunakar Pothuganti b; Analysis of different convolution neural network models to diagnose Alzheimer's disease; Materials Today, 2020; 9: 121- 6. [DOI:10.1016/j.matpr.2020.09.625]



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mazrooei rad E, pazhoumand H, salmani bajestani S. Separation of Healthy Individuals and Patients with Alzheimer's Disease Using the Effective Communication of Brain Signals. Shefaye Khatam 2022; 11 (1) :1-12
URL: http://shefayekhatam.ir/article-1-2334-en.html


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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 11, Issue 1 (Winter 2022) Back to browse issues page
مجله علوم اعصاب شفای خاتم The Neuroscience Journal of Shefaye Khatam
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