|
|
|
|
 |
Volume 10, Issue 1 (Winter 2021) |
 |
|
|
|
|
Comparison of the Function of the Elman Neural Network and the Deep Neural Network for the Diagnosis of Mild Alzheimer's Disease
|
Elias Mazrooei   , Mahdi Azarnoosh * , Majid Ghoshuni , Mohammadmehdi Khalilzadeh |
| Islamic Azad University, Mashhad Branch , M_Azarnoosh@mshdiau.ac.ir |
|
|
Abstract: (2165 Views) |
| Introduction: The main purpose of this study is to provide a method for early diagnosis of Alzheimer's disease. The disease reduces memory function by destroying nerve cells in the nervous system and reducing nerve connections and interactions. Materials and Methods: The level of the disease should be diagnosed according to the association of the disease with various features in the brain signal and medical images. First, with proper preprocessing, nonlinear properties such as phase diagram, correlation dimension, entropy and Lyapunov exponent are extracted and Elman neural network is used for classification. Then, the correctness of the function of Elman neural network is compared with channel neural network. The use of deep learning methods, including channel neural network, can have more appropriate and accurate results among other classification methods. Results: In the case of using two CNN networks and one MLP network, the accuracy of the results was %98 in healthy individuals, %97.7 in mild patients and %97.5 in severely ill patients. In the case of using a CNN network with a combination of features, brain signal and medical images, in the case of stimulation, the accuracy of the results is %95 in healthy individuals, %92.5 in mild patients and %97.5 in severe patients. As a recall, the accuracy of the results is %75 in healthy individuals, %72.5 in mild patients and %87.5 in severe patients. The accuracy of the results in Elman neural network with the combination of brain signal features and medical images is %94.4 and in the case without combination of features, the accuracy of the results is %92.2. Conclusion: Among the processing methods proposed to classify the three classes of healthy, mild patient and severe patient, the method of combining brain signal characteristics and medical images has increased the accuracy of CNN and Elman classifier results. |
|
| Keywords: Alzheimer Disease, Diagnosis, Electroencephalography, Magnetic Resonance Imaging |
|
|
Full-Text [PDF 2668 kb]
(889 Downloads)
|
|
Type of Study: Research --- Open Access, CC-BY-NC |
Subject:
Cognitive Neuroscience
|
|
|
|
|
|
|
| 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. 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. Szirmai, I., Kamondi, A.: EEG investigations in cognitive impairments. Ideggyogy Szemle, 2011; 64: 14-23. 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] 15. Hedges, D., Janis, R., Mickelson, S., Keith, C., Bennett, D., Brown, B.L.: P300 Amplitude in Alzheimer's Disease: A Meta-Analysis and Meta-Regression. Clin EEG Neurosci, 2016; 47: 48-55. [ DOI:10.1177/1550059414550567] 16. P.J.S. Colleen E. Jackson "Electroencephalography and event- related potentials as biomarkers of mild cognitive impairment and mild Alzheimer's disease", 2008; vol. 23: pp. 137-43. 17. X. Wei, L. Zhou, Z. Chen, L. Zhang, Y.i. Zhou, Automatic seizure detection using three-dimensional CNN based on multi-channel EEG, BMC Med. Inf. Decis. Making, 2018; 18 (5): 71-80. [ DOI:10.1186/s12911-018-0693-8] 18. Xiao Jiang, Gui-Bin Bian, Zean Tian, Removal of Artifacts from EEG Signals: A Review, 2019; 987-1002. [ DOI:10.3390/s19050987] 19. Micanovic, C., Suvankar, P.: The diagnostic utility of EEG in early- onset dementia: a systematic review of the literature with narrative analysis. J Neural Transm, 2014; 121 (1): 59-69. [ DOI:10.1007/s00702-013-1070-5] 20. P J Visser, F R J Verhey, P A M Hofman, P Scheltens, J Jolles, Medial temporal lobe atrophy predicts Alzheimer's disease in patients with minor cognitive impairment, J Neurol Neurosurg Psychiatry, 2002; 72: 491-97. 21. Biju K S, Alfa S S, Kavya Lal , Alvia Antony, Akhil M Kurup, Alzheimer's Detection Based on Segmentation of MRI Image, Procedia Computer Science, 2017; 115: 474-81. [ DOI:10.1016/j.procs.2017.09.088] 22. Lies Clerx, Ineke A. van Rossum , Leah Burns , Dirk L. Knol , Philip Scheltens , & et al. Measurements of medial temporal lobe atrophy for prediction of Alzheimer's disease in subjects with mild cognitive impairment, Neurobiology of Aging 34 , 2003e-2013. [ DOI:10.1016/j.neurobiolaging.2013.02.002] 23. Craik, Alexander, Yongtian He, and Jose L. Contreras- Vidal. "Deep learning for electroencephalogram (EEG) classification tasks: a review." Journal of neural engineering, 2019; 16, no. 3: 031001. [ DOI:10.1088/1741-2552/ab0ab5] 24. Zheng, Yi, Qi Liu, Enhong Chen, Yong Ge, and J. Leon Zhao. "Exploiting multi- channels deep convolutional neural networks for multivariate time series classification." Frontiers of Computer Science 10, 2016; no. 1: 96-112. [ DOI:10.1007/s11704-015-4478-2] 25. Zheng, Yi, Qi Liu, Enhong Chen, Yong Ge, and J. Leon Zhao. "Time series classification using multi-channels deep convolutional neural networks." In International conference on web-age information management, 2014; pp. 298-310. [ DOI:10.1007/978-3-319-08010-9_33] 26. Lima, Clodoaldo AM, André LV Coelho, and Marcio Eisencraft. "Tackling EEG signal classification with least squares support vector machines: a sensitivity analysis study." Computers in Biology and Medicine 40, 2010; no. 8: 705-14. [ DOI:10.1016/j.compbiomed.2010.06.005] 27. Morabito, Francesco Carlo, Maurizio Campolo, Cosimo Ieracitano, Javad Mohammad Ebadi, Lilla Bonanno, Alessia Bramanti, & et al. "Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings." In 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 2016; pp: 1-6. IEEE. [ DOI:10.1109/RTSI.2016.7740576] 28. P.J.S. Colleen E. Jackson "Electroencephalography and event- related potentials as biomarkers of mild cognitive impairment and mild Alzheimer's disease," 2008, vol. 23, pp. 137-43.
|
|
mazrooei E, azarnoosh M, ghoshuni M, khalilzadeh M. Comparison of the Function of the Elman Neural Network and the Deep Neural Network for the Diagnosis of Mild Alzheimer's Disease. Shefaye Khatam 2021; 10 (1) :1-11 URL: http://shefayekhatam.ir/article-1-2267-en.html
|
|
|
|
|
|
|
