Methods for Alzheimer’s desiease diagnostics

Main Article Content

Ігор Едуардович Квашений

Abstract

The problem of Alzheimer disease diagnosis is considered. The review of current existing automated methods of Alzheimer disease diagnosis using electroencephalography signals, MRI and SPECT images is given. Efficiencies of these methods are shown. Advantages and disadvantages are presented. Problem of potential redundancy of Alzheimer disease features, which are used in modern diagnosis systems, is considered. Recommendations for the further development of automated methods of Alzheimer disease diagnosis are given, especially application of fuzzy logic approach.

References 29, figures 6

Article Details

How to Cite
Квашений, І. Е. (2014). Methods for Alzheimer’s desiease diagnostics. Electronics and Communications, 19(1), 15–25. https://doi.org/10.20535/2312-1807.2014.19.1.142300
Section
Biomedical devices and systems

References

Alzheimer Disease [Electronic Resource], Mode of access: URL : http://memini.ru/encyclopaedia/111 (Rus)

Abasolo D., Hornero R., Escuerdo P. (2007), “Electroencephalogram Background Activity Characterization with Approximate Entropy and Auto Mutual Information in Alzheimer’s Disease Patients”. Proceeding of the 29th Annual International Conference of the IEEE EMBS Cite Internationale – P. 6191-

Abou-Khalil B., Musilus K. E. (2006), “Atlas of EEG & Seizure Semiology”. Elsevier. Р. 250.

Aggarwal N., Agrawal R. K. (2012), “First and Second Order Statistics Features for Classification of

Magnetic Resonance Brain Images“. Journal of Signal and Information Processing. Pр. 146-153.

Alvarez I., Gorriz J. M., Ramirez J., Sals-Gonzalez D. (2009), “Alzheimer’s diagnosis using eigenbrains and support vector machine”. Electronic Letters Vol. 45 (7). Pр. 342-343.

Cho S. Y., Kim B. Y., and others. (2003), “Automatic Recognition of Alzheimer’s Disease with Single

Channel EEG Recordings”. Proceedings of the 25thAnnual International Conference of the IEEE

EMBS, Cancun, Mexico September. Pр. 2655-2668.

De Bock Th. J., Das S., Mhsin M., and others. (2010), “Early Detection of Alzheimer’s Disease Using

Nonlinear Analysis of EEG via Tsillis Entropy”. Conference Proceeding BSEC. Pр. 1-4.

Gorriz J. M., Ramirez J., Lassl A., and others. (2008), “Automatic Computer Aided Diagnosis Tool using Component-based SVM”. Nuclear Science Symposium Conference Record. Pр. 4392-4395.

Jacques G., Frymiare J. L., Kounis J., and others. (2004), “Multiresolution Analysis for Early Diagnosis

of Alzheimer’s Disease“. Proceeding of the 26th Annual International Conference of the IEEE EMBS.

Pр. 251-254.

Lahmiri S., Boukadoum M. (2012), “Automatic Brain MR Images Diagnosis Based on Edge Fractal

Dimension and Spectral Energy Signature”. 34th Annual International Conference of the IEEE EMBS.

Pр. 6243-6246.

Lopez M., Ramirez J., Gorriz J. M., and others. (2009), “Automatic tool for Alzheimer’s diagnosis using

PCA and Bayessian classification rules”. Electronic Letters Vol. 45 (8). Pр.389-391.

Lopez M., Ramirez J., Gorriz J. M., and others. (2009), “Multivariate approaches for Alzheimer’s disease diagnosis using Bayesian classifiers”. Nuclear Science Symposium Conference Record. Pр.

-3193.

Sheil W. C. (2012), “Magnetic Resonance Imaging (MRI Scan)”. Medicine Net.com

Niedermeyer E., da Silva F. L. (2006), “Electroencephalography: Basic Principles, Clinical Applications, and Related Fields”. Lippincot Williams & Wilkins. Р. 1256.

Novelline R. A. (2004), “Squire's fundamentals of radiology”. Harvard University Press. – 660 pages.

Nowotny P., Known J. M., Goate A. M. (2001), “Alzheimer Disease”. ENCYCLOPEDIA OF LIFE

SCIENCES Nature Publishing

Padilla P., Gorriz J. M., Ramirez J., Chaves R. (2010), “Alzheimer’s disease detection in functional

images using 2D Gabor wavelet analysis”. Electronic Letters Vol. 46 (8). Pр.556-558.

Petrosian A., Prokhorov D., Schiffer R. (1999), “Recurrent neural Network and Wavelet Transform

based Distinction Between Alzheimer and Control EEG”. Proceeding of The First Joint BMES/EMBS

Conference Serving Humanity, Advancing Technology. Pр.1185.

Querfurth H. W., LaFerla F. M. (2010), “Mechanisms of Disease: Alzheimer’s disease”. The New England Journal of Medicine, 362 (4). Pр. 329-344.

Rahmim A., Zaidi H. (2008), “Review article: PET versus SPECT: strengths, limitations and challenges”. Nuclear Medicine Communications Vol. 29 (3). Pр.193-207.

Ramirez J., Chaves R., Gorriz J. M., Lopez M., and others. (2009). “Computer aided diagnosis of the

Alzheimer’s Disease combining SPECT-based feature selection and Random forest classifiers”. Nuclear Science Symposium Conference Record. Pр. 2738-2742.

Ramirez J., Gorriz J. M., Chaves R., and others, (2009) “SPECT image classification using random

forests“. Electronic Letters Vol. 45 (12). Pр.604-605.

Wan B., Gao X., Liu X., and others. (2011), “Electroencephalogram Mutual Information Entropy Analysis for Alzheimer’s Disease”. International Conference on Electrical and Control Engineering (ICECE).

Pр. 4486-4489.

Yagneswaran S., Baker M., Petrosian A. (2002), “Power Frequency and Wavelet Characteristics in

Differentiating Between Normal and Alzheimer EEG” Proceedings of the Second Joint EMES/BMES

Conlerence. Pр.46-47.

Zhang Y., Dong Zh., Wu L. (2011), “A hybrid method for MRI brain image classification”. Expert System with Applications 38. Pр. 1049-1053.

Gnezditsky V. V. (2004), “Inverse problem of EEG and Clinical Electroencephalography (mapping and

locating the source of electrical activity of the brain)”. Moscow : MEDpress-inform. Р. 626. (Rus)

Zenkov L. R. (1996), “Clinical Electroencephalography (with elements of epilepsy)”. Taganrog TSURE

Publishing. Р. 358. (Rus)

Kaufmann A. (1982), “Introduction to the fuzzy sets theory”. Moscow : Radio i svyaz. Р. 432.

Yahyeva G. (2006), “Fuzzy sets and neural networks”. Moscow : BINOM. Р. 316.