Heart Condition Imaging with the Help of the Cardiographic Equipment on Nanosensors

Article Preview

Abstract:

Precision methods and devices for diagnostics of cardiovascular diseases are one of the main directions for development of modern technology in the field of medical instrument engineering. However, there are no small devices that allow for the diagnostics of cardiac muscle with precision accuracy and without operative intervention at this stage. This study presents the problems associated with cardiovascular diseases (CVD) and the analysis of various organizations engaged in development of efficient means for CVD diagnostics. Two-component FitzHugh - Nagumo model and heart condition imaging algorithm are considered. Aspects of work aimed at designing and developing of the hardware and software complex based on the information obtained with the help of an electrocardiograph on nanosensors. The obtained results are presented.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

9-15

Citation:

Online since:

April 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] V. A. Baranov, D. K. Avdeeva, P. G. Pen'kov, M. M. Yuzhakov, I. V. Maksimov, M. V. Balahonova, M. G. Grigoriev, Structural approach to inverse problems of computerized diagnostics in cardiology, Modern problems of science and education. 50 (2013).

Google Scholar

[2] D. K. Avdeeva, O. N. Vylegzhanin, Extraction from registered cardioimpulse orthogonal to it low-amplitude component, Naukovedenie. 19 (2013) (in Russian).

Google Scholar

[3] D. K. Avdeeva, V. Yu. Kazakov, N. M. Natalinova, M. L. Ivanov, The results modeling of high frequency filter and low-pass filter effects to the micropotentials recording quality on the electrocardiogram, Naukovedenie. 19 (2013) (in Russian).

Google Scholar

[4] D. K. Avdeeva, N. V. Turushev, N. M. Natalinova, M. L. Ivanov, Analysis of the impact of filtering links on electroencephalogram and electromyogram registered channel highly sensitive to nanoelectrodes, Naukovedenie. 19 (2013) (in Russian).

Google Scholar

[5] Information on http: /www. who. int/mediacentre/factsheets/fs317/en.

Google Scholar

[6] Information on http: /www. medicus. ru/fphysician/patient/bolezni-kotorye-ubivayut-34765. phtml.

Google Scholar

[7] Bor Kavcic, Electrodynamics of human heart, Seminar 1b-1. year, II. cycle program, University of Ljubljana Faculty of Mathematics and Physics, (2013).

Google Scholar

[8] Information on http: /en. wikipedia. org/wiki/Alexander_Muirhead.

Google Scholar

[9] Information on http: /www. med-group. ru/productdetail/000-000-051/28/0/0.

Google Scholar

[10] Information on http: /www. stormoff. ru/catalog_40_8. html.

Google Scholar

[11] Information on http: /www. davismedical. com/GE-MAC-5500-EKG.

Google Scholar

[12] Information on http: /www. agiannidis-medizintechnik. com/hardware/hellige_cardiosmart_st. htm.

Google Scholar

[13] Yu. E. Yel'kin, The simplest models of excitable media, Mathematical cell (in Russian).

Google Scholar

[14] V. A. Baranov, U. Ewert, Methods of statistical spatial filtering of images on the basis of local group of transformations, Russian Journal of Nondestructive Testing, 2 (2012) 123 - 128.

DOI: 10.1134/s1061830912020039

Google Scholar

[15] V. A. Baranov, U. Ewert, Symmetrical aspects of the causality principle in statistical group-theoretical image-reconstruction methods. Russian Journal of Nondestructive Testing, 3 (2012) 187 - 190.

DOI: 10.1134/s1061830912030023

Google Scholar

[16] V. A. Baranov, U. Ewert, Quasi-tomograpic visualization of crack-formation zones using radiographic projections methods. Russian Journal of Nondestructive Testing, 4 (2012) 245 - 249.

DOI: 10.1134/s1061830912040031

Google Scholar

[17] Grigoriev, M.G., N.V. Turushev D.K. Avdeeva, Human body electroneuromyographic diagnostics device. Journal of Radio Electronics, 4 (2014) (in Russian).

Google Scholar