Last modified: 2019-04-21
Abstract
Researchers are developing methods to extract diagnostic information from brain electrical signals from the Electroencephalogram (EEG) to diagnose brain lesions (malignant tumors, benign tumor, metastasis tumor, abscess).
The aim of the study is to introduce a new diagnostic method based on the normalized constant-correlation function between the reference brain signals (Fingerprint) and the wavelet of the person under test. The study showed that the traditional features of the correlation were unable to diagnose the pathological lesions of the brain effectively because of the nonlinearity of these correlations, which are mostly monotonic non linearity. We studded the following cases:
1) Two similar cases: Inflammatory abscess spread in the parietal lobe of the left cerebral hemisphere.
2) Two non-identical cases: the first has inflammatory abscess spread over the parietal lobe of the left cerebral hemisphere; and the second is infected with malignant tumor spread on the left hemisphere.
3) Two non-identical cases: the first is a person with metastatic melanoma in the frontal lobe of the cerebral hemisphere; the second is a healthy person.
4) Two identical cases of two healthy persons under the same measurement conditions
Keywords: Normalized Constant-Correlation function, Normalized Correlation function, Scatter diagram, Brain electrical signals.
References
[1] JUTGLA, et al , " Diagnosis of Alzheimer’s disease from EEG by means of synchrony measures in optimized frequency bands", pp 4266-4267, 2013.
[2] N.Nisha, E. Dhananjay,"Brain Tumor Detection using ANNs", International Journal of Science and Research (IJSR), Vol 3 Issue 7, 2015.
[3] Anwar .A .G , Sayed .A. M, Munsif .A. J, Muhammad .S, "EEG Signals based Brain Source Localization Approaches", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, 2018.
[4] Musella,"Brain Tumor Symptoms Survey Results" http://www.virtualtrials.com/braintumorsymptomssurvey.cfm, 2017.
[5] M. Dobrovoljac , H. Hengartwer, E. Boltshauser," Delay in the diagnosis of paediatric brain tumours", European Journal of Pediatrics,VOL 161 (12),pp 663–667,2005.
[6] B.Husin, L.Jancke, B.Tag, "Impact Assessment of Neuroimaging", vdf Hochschulverlag AG an der ETH Zürich, 2006.
[7] Муфди .Ф. Х, " Исследование свойств и разработка средств измерения конкорреляционных и векторных характеристик связи случайных сигналов" , http: //tekhnosfera.com/issledovanie-svoystv-i-razrabotka-sredstv-izmereniya-konkorrelyatsionnyh-i-vektornyh-harakteristik-svyazi-sluchaynyh-sign#ixzz2oueJQ2VI,1993.
[8] Пат. 2118847 РФ, МКИ *, Измеритель временной задержки сигналов в динамических системах / Губарев В.В., Муфди Ф.Х., Угрюмова О.В. // Изобретения. № 25. – C.325,1998.
[9] Gubarev. V.V, Mufdi H.F, "Non-Linear Probability Models and Problems of their Application" , Proceedings of the Second Indo-Russian Joint Workshop on Computational Intelligence and Modern Heuristics in Automation and Robotics. 20, Karl Marx Prospekt, Novosibirsk,2011.
[10] D. S. Moore, W. I. Notz, M. A. Flinger, "The basic practice of statistics" (7th ed.). New York, NY: W. H. Freeman and Company,2016
[11] Dennis E. Hinkle ,William. W, Stephen G. Jurs, - "Applied Statistics for the Behavioral Sciences", 5th Edition,Boston, Mass. 2003
[12] Laura D. G, Nancy L. L, "Understanding Correlation: Factors That Affect the Size of r", The Journal of Experimental Education, 74(3), pp 251–266,2006
[13] M,M, Mukaka,"Statistics Corner: A guide to appropriate use of Correlation coefficient in medical research" , The Liverpool School of Tropical Medicine, Liverpool, L69 3GA, UK, University of Liverpool. ,2013
[14] Hirsch L., Brenner R., 2010 – "Atlas of EEG in Critical Care", John Wiley & Sons, Ltd, Chichester, UK. P:1:6.