International Science Index


Early Diagnosis of Alzheimer's Disease Using a Combination of Images Processing and Brain Signals


Alzheimer's prevalence is on the rise, and the disease comes with problems like cessation of treatment, high cost of treatment, and the lack of early detection methods. The pathology of this disease causes the formation of protein deposits in the brain of patients called plaque amyloid. Generally, the diagnosis of this disease is done by performing tests such as a cerebrospinal fluid, CT scan, MRI, and spinal cord fluid testing, or mental testing tests and eye tracing tests. In this paper, we tried to use the Medial Temporal Atrophy (MTA) method and the Leave One Out (LOO) cycle to extract the statistical properties of the three Fz, Pz, and Cz channels of ERP signals for early diagnosis of this disease. In the process of CT scan images, the accuracy of the results is 81% for the healthy person and 88% for the severe patient. After the process of ERP signaling, the accuracy of the results for a healthy person in the delta band in the Cz channel is 81% and in the alpha band the Pz channel is 90%. In the results obtained from the signal processing, the results of the severe patient in the delta band of the Cz channel were 89% and in the alpha band Pz channel 92%.

[1] Alzheimer’s Association, “Alzheimer’s Disease Statistics,” Available at: http;// AD/statistics.asp. 24/7 Helpline: 1.800.272.3900. 2015-2016.
[2] 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, vol. 3, pp. 327-330, 2009.
[3] P. J. S. Colleen E. Jackson “Electroencephalography and event-related potentials as biomarkers of mild cognitive impairment and mild Alzheimer’s disease,” vol. 23, pp. 137-143, 2008.
[4] Polikar R., Topalis A., Green D., Kounios J., Clark C. M., Ensemble based data fusion for early diagnosis of Alzheimer’s disease, Information Fusion, vol. 9, no. 1, pp. 83-95, 2008.
[5] R. A. Sadek, “An Improved MRI Segmentation for Atrophy Assesment”, Int. Journal of Computer Science Issues, (2012) June.
[6] Y. Zhang and L. Wu, “Fast Document Image Binarization Based on an Improved Adaptive Otsu’s Method and Destination Word Accumulation”, Journal of Computational Information Systems, (2011), pp. 1886-1892.
[7] Burton, E. J., Barber, R., Mukaetova-Ladinska, E. B., Robson, J., Perry, R. H., Jaros, E., Kalaria, R.N., O'Brien, J.T., 2009. Medial temporal lobe atrophy on MRI differentiates.
[8] Alzheimer's disease from dementia with Lewy bodies and vascular cognitive impairment: a prospective study with pathological verification of diagnosis. Brain 132, 195e203.
[9] Engedal, K., Snaedal, J., Hoegh, P., Jelic, V., Bo Andersen, B., Naik, M., Wahlund, L.O., Oeksengaard, A.R., 2015. Quantitative EEG applying the statistical recognition pattern method: a useful tool in dementia diagnostic workup. Dement. Geriatr. Cogn. Disord. 40, 1e12.
[10] Tejash Patel., Robi Polikar., Senior Member., IEEE, Christos Davatzikos, Christopher M. Clark. EEG and MRI Data Fusion for Early Diagnosis of Alzheimer’s Disease. 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008.
[11] Takahashi, R., Ishii, K., Miyamoto, N., Yoshikawa, T., Shimada, K., Ohkawa, S., Kakigi, T., Yokoyama, K., 2010. Measurement of gray and white matter atrophy in dementia with Lewy bodies using diffeomorphic anatomic registration through exponentiated lie algebra: a comparison with conventional voxelbased morphometry. AJNR. Am. J. Neuroradiol. 31, 1873e1878.
[12] Treglia, G., Cason, E., 2012. Diagnostic performance of myocardial innervation imaging using MIBG scintigraphy in differential diagnosis between dementia with lewy bodies and other dementias: a systematic review and a meta-analysis. J. Neuroimaging 22, 111e117.
[13] Watson, R., O'Brien, J.T., Barber, R., Blamire, A.M., 2012. Patterns of gray matter atrophy in dementia with Lewy bodies: a voxel-based morphometry study. Int. Psychogeriatrics 24, 532e540.
[14] Sean J,. Colloby., Ruth A. Cromarty, Luis R. Peraza, Kristinn Johnsen, Gísli J_ohannesson, Laura Bonanni, Marco Onofrj, Robert Barber, John T. O'Brien, John-Paul Taylor. Multimodal EEG-MRI in the differential diagnosis of Alzheimer's disease and dementia with Lewy bodies. Journal of Psychiatric Research 78 (2016) 48e55.
[15] Rowayda A,. Sadek,. Regional Atrophy Analysis of MRI for Early Detection of Alzheimer's Disease. International Journal of Signal Processing, Image Processing and Pattern. Recognition Vol. 6, No. 1, February, 2013.
[16] Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods. Peyman Goli, Elias Mazrooei Rad, Kavian Ghandehari, Mehdi Azarnoosh. Machine Learning Research Volume 2, Issue 4, December 2017, Pages: 148-151 Received: Oct. 23, 2017; Accepted: Nov. 10, 2017; Published: Dec. 15, 2017.