Visn. Nac. Akad. Nauk Ukr. 2021.(2): 33-43

Illya A. Chaikovsky
Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine, Kyiv, Ukraine

Mykhailo A. Primin
Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine, Kyiv, Ukraine

Anatolii P. Kazmirchuk
National Military Medical Clinical Center "Main Military Clinical Hospital"


The article details the concept of clinical information technology (IT), i.e. a set of methods and software and hardware combined into a technological chain, the product of which is an automated diagnostic report, prognostic report or recommendation on patient management tactics. There are several examples of innovative information technologies and metrics implemented by the authors in Ukraine and abroad, designed to register and evaluate subtle changes in the electromagnetic field of the heart for early diagnosis of the most common and dangerous heart diseases, especially coronary heart disease. It is shown that new metrics of analysis of spatial structure of 2D and 3D magnetocardiographic maps of current density distribution allow to diagnose with high accuracy various forms of myocardial ischemia. The new method of the electrocardiogram scaling is used in various areas of clinical medicine, sports medicine, occupational medicine, as well as in large-scale population studies.
Keywords: information technologies, metrics, clinical cybernetics, subtle changes, magnetocardiography, electrocardiography, coronary heart disease.

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  1. Benjamin E.J., Virani S.S., Callaway C.W. et al. Heart Disease and Stroke Statistics–2018 Update: A Report From the American Heart Association. Circulation. 2018. 137(12): 67–492. DOI:
  2. Center for Medical Statistics of the Ministry of Health of Ukraine.
  3. Simoons M.L., Hugenholtz P.G. Estimation of the probability of exercise induced ischemia by quantitative ECG analysis. Circulation. 1977. 56(4): 552–559. DOI:
  4. Chaikovsky I.A., Wojtowich I.D. Approaches to the evaluation of the maturity degree of clinical information technologies by the example of technologies of analysis of the electrical activity of heart. Dopov. Nac. Akad. Nauk Ukr. 2014. (2): 160–167. (in Russsian). DOI:
  5. Baule G., McFee R. Detection of the magnetic field of the heart. American Heart Journal. 1963. 66(1): 95–96. DOI:
  6. Cohen D., Edelsack E.A., Zimmerman J.E. Magnetocardiograms taken inside a shielded room with a superconducting point-contact magnetometer. Appl. Phys. Lett. 1970. 16(7): 278–280. DOI:
  7. Primin M., Nedayvoda I. Mathematical model and measurement algorithms for a dipole source location. International Journal of Applied Electromagnetics and Mechanics. 1997. 8(2): 119–131.
  8. Primin M., Nedayvoda I. Inverse problem solution algorithms in magnetocardiography: new analytical approach and some results. International Journal of Applied Electromagnetics and Mechanics. 2009. 29(2): 65–81. DOI:
  9. Primin M.A., Nedayvoda I.V. A Method and an Algorithm to Reconstruct the Spatial Structure of Current Density Vectors in Magnetocardiography. Cybernetics and Systems Analysis. 2017. 53(3): 485–494. DOI:
  10. Hailer B., Chaikovsky I., Auth-Eisernitz S., Schäfer H., Steinberg F., Grönemeyer D.H.W. Magnetocardiography in CAD with a new system in an unshielded setting. Clinical Cardiology. 2003. 26(10): 465-471. DOI:
  11. Chaikovsky I., Hailer B., Sosnytskyy V., Lutay M., Mjasnikov G., Kazmirchuk A., Budnyk M., Lomakovskyy A., Sosnytskaja T. Predictive value of the complex magneto-cardiographic index in patients with inter-mediate pretest probability of chronic coronary artery disease: results of a two-center study. Coronary Artery Disease. 2014. 25(6): 474-484. DOI:
  12. Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart. A Statement for Healthcare Professionals From the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation. 2002. 105(4): 539–542. DOI:
  13. Chaikovsky I., Primin M., Nedayvoda I., Mjasnikov G., Kazmirchyk A., Lutay M., Stadnyk L., Ji W., Lei M. Monitoring of myocardial viability in patients with myocardial infarction based on magnetocardiographic analysis of ventricular depolarisation. Journal of the American College of Cardiology. 2018. 72(16): C89. DOI:
  14. Colan S.D. The Why and How of Z-Scores. JASE. 2013. 26(1): 38-40. DOI:
  15. Chaikovsky I. Electrocardiogram scoring beyond the routine analysis: subtle changes matters. Expert Review of Medical Devices. 2020. 17(5): 379–382. DOI:
  16. Chaikovsky I., Kryvova O., Kazmirchuk A. et al. Assessment of the Post-Traumatic Damage of Myocardium in Patients with Combat Trauma Using a Data Mining Analysis of an Electrocardiogram. 2019 Signal Processing Symposium (SPS). P. 34-38. DOI:
  17. Neary J.P., Baker T., Jamnik V. et al. Multimodal Approach to Cardiac Screening of Elite Ice Hockey Players During the NHL Scouting Combine. Medicine & Science in Sports & Exercise. 2014: 46:742. DOI:
  18. Chaikovsky I., Lebedev E., Ponomarev V., Necheporuk A. The relationship between ECG/HRV variables and socio-economic factors: results of mass screening in the rural region of Ukraine. European Journal of Preventive Cardiology. 2020. 27(1): 92. DOI:
  19. Clarke R., Chaikovsky I., Wright N., Du H., Chen Y., Guo Y., Bian Z., Li L., Chen Z. Independent relevance of left ventricular hypertrophy for risk of ischaemic heart disease in 25,000 Chinese adults. European Heart Journal. 2020. 41(2): ehaa946.2938. DOI:
  20. U.S. Patent US10512412В2. Chaikovsky I., Starynska G., Budnyk M. Method of ECG evaluating based on universal scoring system. 2020.