N. S. Konnova – Ph.D. (Eng.), Associate Professor of Bauman Moscow State Technical University

M. A. Basarab – Dr.Sc. (Phys.-Math.), Head of Department of Information Security of Bauman Moscow State Technical University

E-mail: bmic@mail.ru

D. A. Basarab – Ph.D. (Med.), Head of Cardiovascular Department of St. Ioasaf's Belgorod Regional Hospital (Belgorod)

D. V. Minin – Post-graduate Student, Bauman Moscow State Technical University

V. M. Achildiev – Ph.D. (Eng.), Chief Designer, SPU Geophizika-NV (Moscow)

V. A. Soldatenkov – Dr.Sc. (Eng.), General Director of SPU Geophizika-NV (Moscow)

N. A. Bedro – Head of Department, Deputy Chief Designer, SPU Geophizika-NV (Moscow)

Yu. K. Gruzevich – Ph.D. (Eng.), Deputy General Director for Science, SPU Geophizika-NV (Moscow)

Yu. N. Evseeva – SPU Geophizika-NV (Moscow)

A. D. Levkovich – Ph.D. (Eng.), SPU Geophizika-NV (Moscow)

M. N. Komarova – Leading Engineer, SPU Geophizika-NV (Moscow)

There are presented the concept and implementation of a decision support system in cardiology on the basis of various recorded indices of the human cardiovascular system: electro-, seismic cardiography, flowmetry, etc. Analysis of developments in the field of machine learning methods application to decision support problems in cardiology has been given: the achievable values of the cardiovascular system states classification accuracy have been considered when using neural networks of various architectures and various learning algorithms.

The focus of the article is on the possibilities of application and preparation of seismic cardiography data for use in the diagnostics. Analysis of the seismic cardiography data parameters variability (from person to person and in different states of one person) has been given, in particular with respect to the characteristic points taken into account in phase analysis. The results and discussion of spectra, noise characteristics, phase portraits, autocorrelation functions of signals of angular rates and apparent accelerations along three axes have been presented. The methods of digital signal processing which are most suitable for use in diagnostics according to the SCG, including using machine deep learning, have been identified.

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2. Konnova N.S., Basarab M.A., Basarab D.A. Image processing using artificial intelligence methods in cardiovascular decision support systems // Proc. SPIE 10836. 2018 International Conference on Image and Video Processing, and Artificial Intelligence. DOI: 10.1117/12.2515343
3. Konnova N.S., Basarab M.A. Identification of functional states of the cardiovascular system according to flowmetry data using machine learning methods // Proc. of 11th International Symposium on Computational Intelligence and Design (ISCID 2018). 2018. In press.
4. Konnova N.S., Basarab M.A., Basarab D.A. Tsifrovaya obrabotka signalov v doplerovskoj floumetrii. Metody i algoritmy. Monografiya. Lambert Academic Publishing. 2018.
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