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Issue 4 (194), article 4

DOI:https://doi.org/10.15407/kvt194.04.061

Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Kozak L.M., DSc (Biology), Senior Researcher,
Leading Researcher of the Medical Information Systems Department
e-mail: lmkozak52@gmail.com

Kovalenko A.S., DSc (Medicine), Professor,
Head of the Medical Information Systems Department
e-mail: askov49@gmail.com

Krivova O.A., Researcher of the Medical Information Systems Department
e-mail: ol.kryvova@gmail.com

Romanyuk O.A., Junior Researcher of Medical Information Systems Department
e-mail: ksnksn7@gmail.com

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Glushkov ave., 40, Kiev, 03187, Ukraine

DIGITAL TRANSFORMATION IN MEDICINE: FROM FORMALIZED MEDICAL DOCUMENTS TO INFORMATION TECHNOLOGIES OF DIGITAL MEDICINE

Introduction. According to the Concept of Ukraine`s Digital Economy and Society Development in 2018-2020, the key components of “digitalization” are the development of digital infrastructure — broadband Internet throughout Ukraine, and the promotion of digital transformations in various sectors of the economy and society, including medicine.

The purpose of the paper is to analyze the stages of digital transformation in medicine and the results of authors and their colleagues of the MIS department for the development of information technologies of digital medicine.

Results. A generated model of digital transformation in medicine is presented and several main stages of this transformation are highlighted: І — digital transformation of primary medical information; ІІ — development of support systems for the diagnostic and treatment process; ІІІ — development of technologies and systems for supporting the physicians` activities with digital information; IV — mobile medicine; V — the digital medicine globalization. The method of determining the markers of the functional state of the cardiovascular system based on mathematical models of forecasting and classification with the use of Data Mining is proposed. The method allows detecting and determining the prognostic values of ECG parameters of the CVS functional state for different groups of patients. The developed IT for supporting the processes of receiving, transmitting and storing digital medical images is aimed at ensuring the effective operation of a physician with digital information from various sources: functional diagnostic complexes, digital medical data storage and images using Picture Archiving and Communication Systems (PACS) and cloud technologies . The proposed telemedicine systems theory including the formulated principles of organizing these systems, criteria and methods for analyzing digital medical data has been implemented for elaborating and functioning the Telemedicine Centre. It enables to cover the population in more than 20 Ukraine`s regions with qualified medical assistance.

Conclusions. The digital transformation in medicine like any new process takes place with a gradual complication of tasks, methods and means of their implementation: from formalization of primary medical information to improvement of methods of its analysis, transfer and storage to improve the quality of medical care for patients at any point of the world.

Keywords: digital transformation in medicine, formalized medical records, Data Mining, IT for assessing human state and physiological systems` state, telemedicine, m-medicine.

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REFERENCES

1. About the conceptualization of the concept of the development of the digital economy of Ukraine and 2018–2020 on the basis of the plan set for the project: Disposition of the Government of Ukraine. URL: http://www.me.gov.ua/Documents/ (Last accessed: 06.07.18) (in Ukranian).

2. The Nine Elements of Digital Transformation. URL:  https://sloanreview.mit.edu/article/the-nine-elements-of-digital-transformation/?social_token=d65abc6db70ba459408562abb8de32bc &utm_source= facebook&utm_medium=social&mmmmmt (Last accessed: 27.06.18)

3. Medical information system. Kyiv: Nauk. Dumka, 1975. 508 p. (in Russian).

4. A. p. № 2002032456 Ukraine MKI. Method for the diagnosis of local changes in the myocardium state. V.A. Petrukhin, V.N. Mamaev, A.S. Kovalenko, T.V. Petrukhina, V.A. Shumakov. Announced 15.01.2003; publ. 03.28.2003. (in Russian).

5. Provotar A.I., Vasilik P.V. Model waves and interaction: Theoretical and applied as-pects. Kyiv: Nauk. Dumka, 2014. 296 p (in Russian).

6. Vasilik P.V., Lychak M.M. Possible interactions in the Solar System and the synchronism of cyclical variations in solar activity with climatic changes on Earth. Geophysical journal. 2012. V. 34, No. 1. P. 138–158. (in Russian).

7. Vasilik P.V., Vasilega A.G., Chekaylo M.A. Influence of disturbances of space environmental factors on the accident rate of the objects of ground infrastructure and the accident rate on transport. Kibernetika i vyčislitel`naâ tehnika, 2011, Issue 166. P. 74–84 (in Russian).

8. Kozak L.M., Lukashenko M.V. The use of information models and integral assessments of the functional state of students for the formation of programs of psychological support. Integrative anthropology. 2008. №2 (12). P. 51–57 (in Russian).

9. Kozak L.M., Lukashenko M.V. Monitoring and correction students ’functional state. Kibernetika i vyčislitel`naâ tehnika. 2014. Issue 176. P. 74–84.

10. M.L. Kochina, L.M. Kozak, A.S. Yevtushenko Analysis of changes in the factor structures of indicators of the functional state of a person with different types of visual load. Bulletin of problems of biology and medicine. 2013, Iss. 1, Vol. 1 (98), pp. 41–45 (in Russian).

11. Evtushenko A.S., Kozak L.M., Kochina M.L. Evaluation of the relationship structure be-tween the functional indicators of operators in visual work using factor models. Kiber-netika i vyčislitel’naâ tehnika. 2016. Vol. 185. P. 60–76 (in Russian).

12. Rogozinskaya N.S., Kozak L.M. Information support of technology for automated moni-toring of the health of the population. Kibernetika i Sistemnyj Analiz. 2013. № 6. P. 162-173 (in Russian).

13. Rogozinskaya N.S., Kozak L.M. Complex indicators for the analysis of causal mortality of the population. Clinical informatics and telemedicine. 2013. Vol. 9, Iss. 10. P. 108–116 (in Russian).

14. Rogozinskaya N.S., Kozak L.M. Information technology research of the state of health of the population of the region. Upravlâûŝie sistemy i mašiny. 2013. № 6. P. 59–67 (in Russian).

15. Krivova O.A., Kozak L.M. Comprehensive assessment of regional demographic devel-opment. Kibernetika i vyčislitel`naâ tehnika. Issue 182. 2015. P. 70–84 (in Russian).

16. Krivova, OA, Tchaikovsky, I.A., Kalnish, VV, Kozak, L.M. Vidbіr informative shows the variability of the rhythm of the heart – the markers of the reaction to his stimulation. Medical informatics and engineering. 2016. No. 2. pp. 37–44 (in Russian).

17. Tchaikovsky I.A. The concept of multilateral analysis of the electrocardiogram using portable electrocardiographs as part of a preventive medical examination. Preventive medicine. 2014. No. 17 (2). P. 42–48 (in Russian).

18. Sposib Universalnoyi Balnoyi Otcinky EKG: Budnik M.M., Staryska G.A., Tchaikovsky І.A. Pat. 104827 Ukraine, IPC: A61B 5/0402, A61B 5/0205; declare 07.13.2015; publ. 02/25/2016, Bul. No. 4.

19. EN ISO 12052: 2011. Health informatics. Digital work, including workflow and data management URL: http://iso.org. (Last accessed: 23.01.18)

20. Romanyuk O. A., Kovalenko A.S., Kozak L.M. Information support interoperability of instrumental studies and long-term storage of digital medical imaging in health care sys-tem. Kibernetika i vyčislitel`naâ tehnika. 2016. Iss. 184. P. 56–71 (in Russian).

21. Kovalenko A.S., Kozak L.M., Romanyuk O.A. Information technology of digital medi-cine. Kibernetika i vyčislitel`naâ tehnika. 2017. №1(187). P.67–79. (in Russian).

22. Kovalenko A.S., Kozak L.M., Ostashko V.G. Telemedicine — the development of a sin-gle medical information space. Upravlâûŝie sistemy i mašiny. 2005. № 3. P. 86–92 (in Russian).

23. Gritsenko V.I., Kozak L.M., Kovalenko A.S., Pezenzali A.A., Rogozinskaya N.S., Ostashko V.G. Medical information systems as elements of a unified medical informa-tion space. Kibernetika i vyčislitel`naâ tehnika, 2013, Iss. 174. P 30-46 (in Russian).

24. Kovalenko O.S., Kozak L.M., Romaniuk O.O., Maresova T.A., Nenasheva L.V., Fyniak G.I. Mobile applications in the structure of modern medical information systems. Upravlâûŝie sistemy i mašiny, 2018, №4. P. 57-69.

 

Received 29.08.2018

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Issue 2 (192), article 4

DOI:https://doi.org/10.15407/kvt192.02.061

Kibern. vyčisl. teh., 2018, Issue 2 (192), pp.

Buzynovsky А.B.1,
PhD student,
e-mail: arturdoc1983@ukr.net
Kovalenko A.S.1,
D.Sci. (Medicine), Professor,
Head of Medical Information Systems Department
e-mail: alexkovalenko@yandex.ua
Bayazitov N.R.2,
D.Sci. (Mdicine),
Professor at the Surgery Department
e-mail: ics_video@ukr.net
Godlevsky L.S.2,
D.Sci. (Medicine), Professor,
Chief of the Department of Biophysics, Informatics and Medical Devices
e-mail: godlevskyleonid@yahoo.com

1International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Acad. Glushkova av., 40, Kiev, 03187, Ukraine
2Odessa National Medical University,
Valekhovsky Lane, 2, Odessa, 65082, Ukraine

THE EFFECTIVENESS OF SURGEON DECISION ON PAIN SYNDROME OF PELVIC ORIGIN TREATMENT IN WOMEN ESTIMATED WITH THE MODEL OF DECISION TREE

Introduction. The problem of correct diagnostics with the decision on the consequent adequate treatment of diseases which are causative for pelvic pain syndrome in women is actual for 15–24% women of fertile age.
The purpose of the work is to investigate the effectiveness of different methods of treatment women with pain syndrome originated from pelvis and lower part of abdomen on the basis of retrospective analysis of 1092 histories of diseases during 2013–2017 р.р.
Methods. Method of decision tree building up was used. The probability of different outcomes — restoration of health, recurrence of the disease along with the perioperative complications as well as duration of treatment in each case were taken into consideration as informative indices for decision tree composing. On the basis of mentioned data the index of effective period of treatment (EPT) was calculated. Period of observation was six months from the moment of disease diagnostics.
Results. It was established that the probability of complete health restoration was 0,83 after surgical treatment and 0,62 after drug treatment. In case of initial inefficiency of drug treatment the probability of restoration of health as a result of surgical intervention was 0,40. The EPT in surgically treated patients was less than EPT in patients with therapeutic treatment by 3,29 times at the moment of making decision on the method of treatment.
Conclusions. It was concluded that early decision on surgical intervention as a method of diagnostics and treatment was more effective when compared with the drug method of treatment women with pelvic pain syndrome. Dependence of the treatment effects upon perioperative complications serve as forecasting data for individual medical care delivered during postoperative period.

Keywords: tree of decision, undertaking of decision in surgery, pain syndrome, the effectiveness of treatment estimation.

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REFERENCES

1 Lyashenko A.V., Bayazitov N.R., Godlevsky L.S. et al. Informational-technical system for automatized laparoscopic diagnostics. Radioelectronics, computer sciences and control. 2016. No 4. P. 90–96. (in Ukrainian).

2 Egorov A.A., Mikshina V.S. The models of surgeon decision. Letters on New Medical Technologies, 2011, Vol. 7, No4. P. 178–81. (in Russian)

3 Litvin A.A., Litvin V.A. Systems of decision support in surgery. News of Surgery, 2014. No 1. P. 96–100. (in Russian)

4 Kucey D.S. Decision analys is for the surgeon. World J.Surg, 1999. Vol. 23. P. 1227–1231. https://doi.org/10.1007/s002689900653

5 Sears E.D., Chung K.C. Decision analysis in plastic surgery: A Primer. PlastReconstrSurg. 2010. Vol. 126, N4. P. 1373–1380. https://doi.org/10.1097/PRS.0b013e3181ead10a

6 Ozerskaya I.A., Ageeva M.I. Chronic pelvic pain in women of fertile age. Ultrasonic diagnostics. Moscow 2009. 299 p. (in Russian)

7 Kiryanov B.F., Tokmachov M.S. Mathematical models in health care: text-book. Novgorod, 2009. 279 p. (in Russian)

8 Rudenko S.V., Romanenko M.V., Katunina O.G., Kolesnikova E.V. Markov models of patients state changes in projects of delivering medical service. Control of complicated systems development. 2012. No12. P. 86–89. (in Ukrainian)

9 Detsky A.S., Nagile G., Krahnetal M.D. Primer on medical decision analysis: Part 2. Building a tree. MedDecis Making. 1997. 7. P. 126–135. https://doi.org/10.1177/0272989X9701700202

10 Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and regression trees. Monterey, CA 1984. 368 p.

Reseived 12.10.2017

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Issue 1 (187), article 5

DOI: https://doi.org/10.15407/kvt187.01.067

Kibern. vyčisl. teh., 2017, Issue 1 (187), pp.67-80

Kovalenko A.S., Dr Medicine, Prof., Head of Department of Medical Information Systems
e-mail: askov49@gmail.com
Kozak L.M., Dr Biology, Leading Researcher of Department of Medical Information Systems
e-mail: lmkozak52@gmail.com
Romanyuk O.A., Junior Researcher of Department of Medical Information Systems
e-mail: ksnksn7@gmail.com

International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and Ministry of Education and Science of Ukraine,
av. Acad. Glushkova, 40, Kiev, 03680, Ukraine

INFORMATION TECHNOLOGY FOR DIGITAL MEDICINE

Introduction. The need of health care institutions in the repeated use of digital medical images by different specialists during patient care and long-term storage for the analysis of diagnostic information determines the relevance of this work. The need for means and methods of storage of digital medical data with their subsequent processing and analysis, as well as on mobile devices for the collection, digital data processing and exchange increase.

The purpose of the article is to analyze the experience of creating medical information systems, the development of information technology support the storage and processing of digital medical information and the further development of information technology for digital medicine.

Results. Employees of the department of medical information systems for more than 20 years of activities of the International Research and Training Centre for Information Technologies and Systems NAS and MES of Ukraine solved the problem of constructing the medical information systems and information diagnostics technologies with the use of electronic medical records, methods and means of the mathematical analysis of medical data. The developed technology support for storing and processing digital medical information combines into a single functional network the medical information system, instrumental diagnostic systems and a system of conservation and archiving digital medical images. PACS and cloud technologies was used for long-term storage of digital medical images.

Conclusion. Organization of long-term storage of digital medical images obtained from the diagnostic systems in health care facilities, and the ability to use this information by doctors at their workplace in the current diagnostic and treatment process was provided by using the developed information technology support for storing and processing digital medical information.

Keywords: medical information system, information technology, digital medical imaging, long-term storage.

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REFERENCE

  1. Amosov N.M. Voices of Times. Kiev, Oranta-Press, 1999. 500 p. (in Russian).
  2. Medical Information System. Kiev: Nauk. Dumka, 1975. 508 p. (in Russian).
  3. Kozak L.M., Elizarov V.A. Automated system for determining the characteristics of the intellectual and emotional components of the human mental health. Ukrainskiy zhurnal med.tekhniki ta tekhnologii. 1995. Iss. 3. P. 59–66 (in Russian).
  4. Garbuz Y.I., Bogatyreva R.V., Kovalenko A.S. et al. National Computer Information System for monitoring the epidemic process in Ukraine. Monitoring technology. Vrachebnoye delo. 1999. №3. P. 3–12 (in Russian).
  5. Sinekop Y.S., Kovalenko O.S., Matrosova G.A. The use of computer technology to analyze problems of health and medical diagnostics. Elektronika i svyaz. 2001. №11. P. 114–117 (in Russian).
  6. Budnyk M., Kovalenko A., Berezovska T., Kozlovsky V. Finding of ROI Intervals at Cardiocycle for CAD Study in Magnetocardiography. International Journal of Bioelectromagnetism. 2003. Vol. 5, N 1. P. 107–108.
  7. Kozak L.M., Lukashenko M.V. Monitoring and correction students’ functional state by the information technology tools. Cybernetics and Computer Engineering. 176. 2014. P. 74–84.
  8. Yevtushenko A.S., Kozak L.M., Cochina M.L., Yaworskij A.V. The results of the use of factor models for the evaluation of human functional state when the visual work. Ukrainskiy zhurnal meditsini, biologii ta sportu. 2015. № 2 (2). P. 70–74 (in Russian).
  9. Rogozinskaya N.S., Kozak L.M. Dataware technologies automated health monitoring. Kibernetika i sistemnyy analiz. 2013. № 6. P. 162–173 (in Russian).
  10. Krivova O.A., Kozak L.M. Comprehensive assessment of regional demographic development. Kibernetika i vyčislitelnaâ tehnika. 182. 2015. P. 70–84 (in Russian).
  11. Vasylyk P.V., Kovalenko A.S., Bychkov V.V., Vasilega A.G. The role of environmental factors in the occurrence of bursts of accidents on the roads. Ukraina. Zdorov’ya natsii. 2008. №3–4 (7–8) p. 35–40 (in Russian).
  12. Bychkov V.V. Theoretical basis of forecasting the risks of emergencies. Environment and health. 2009. № 4 (51). P. 12–14 (in Ukrainian).
  13. Provotar A.I. Vasylyk P.V. Model wave and interaction: theoretical and applied aspect. Kiev: Nauk. Dumka. 2014. 296 p. (in Russian).
  14. Kovalenko O.S., Golubchikov N.V., Orlova N.M. Information systems in health and basic requirements for their creation. Management of health care. №1. 2007. P. 42–46 (in Ukrainian).
  15. Litvinov A.A., Kovalenko A.S., Golubchikov M.V. Features of the design of the subsystem “Statistics” hospital information system. Control systems and machine. 2007. №5. P. 61–67 (in Russian).
  16. Pezentsali A.A., Kozak L.M., Kovalenko A.S. Functional model as the basis for management of the project to develop an integrated information system of medical institution. Control systems and machines. 2008. № 4. P. 3–10 (in Russian).
  17. Kovalenko A.S., Kozak L.M., Ostashko V.G. Telemedicine — the development of a single medical information space. Control systems and machines. 2005. № 3. P. 86–92 (in Russian).
  18. Lazorishinets V.V., Slabkiy G.O., Kovalenko O.S et al. Health Informatization of Ukraine and the development of telemedicine. Zhytomyr: Polissya, 2010. 228 p. (in Ukrainian).
  19. Gritsenko V.I., Kozak L.M., Kovalenko A.S. et al. Medical information systems as part of a single medical information space. Kibernetika i vyčislitelnaâ tehnika. 2013. 174. P. 30–46 (in Russian).
  20. Kovalenko O.S., Buryak V.I. Standardization of information systems of health care on the basis of European integration. Clinical Informatics and Telemedicine. 2004. № 1. P. 35–40 (in Ukrainian).
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  22. Romaniuk O. A., Kovalenko A.S., Kozak L.M. Information support interoperability of instrumental studies and long-term storage of digital medical imaging in health care system. Kibernetika i vyčislitelnaâ tehnika. 2016. 184. P. 56–71 (in Russian).
  23. The system of storage and processing of medical images. Medical grid. URL:http://medgrid.isma.kharkov.ua. Free (29.09.2016)

Recieved 27.12.2016

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Issue 184, article 5

DOI:https://doi.org/10.15407/kvt184.02.056

KVT, 2016, Issue 184, pp.56-72

UDC 004.75+004.932.2:616

INFORMATION SUPPORT FOR INTEROPERABILITY OF INSTRUMENTAL DIAGNOSTIC SYSTEMS AND LONG-TERM STORAGE SYSTEM OF DIGITAL MEDICAL IMAGES IN HEALTH CARE INSTITUTIONS

Romanyuk O.A., Kovalenko A.S., Kozak L.M.

International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Kiev, Ukraine

ksnksn7@gmail.com , askov49@gmail.com , lmkozak52@gmail.com

 

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Introduction. The need of health care institutions in the repeated use of digital medical images by different specialists during patient care and long-term storage for the analysis of diagnostic information determines the relevance of this work.

The purpose of the article is to form technique for adapting the system of transmission and archiving of digital medical images according to the conditions of the health care institution.

Results. Analysis of medical image storage systems to select the specific software implementation according to the requirements of a particular medical institution was held. After testing and validation in working conditions PACS Conquest DICOM has been selected, its functionality has been verified. The technique of adapting the system for transmission and archiving of digital medical images has been developed, which includes an analysis of instrumental studies systems, installation of database components and configuration of PACS Conquest DICOM-server, connecting the instrumental studies systems and organization of access to digital medical images for medical personnel. Testing of the proposed technique was carried out at the National Institute of Cardiovascular Surgery named by N. Amosov.

Conclusion. The possibility of implementation the free and multi-platform storage system with connection of multiple diagnostic systems for efficient analysis of DMC on each workstation and long-term storage DMC is provided as a result of using the proposed technique.

Keywords: digital medical imaging, standard DICOM, long-term storage.

References:

  1. Kamenshchikov A.A. Interoperability in the field of e-health. Information Technologies and Computing Systems, 2009, № 5, pp. 67–71(in Russian).
  2. Robert Hoyt, Nora Bailey, Ann Yoshihashi. Health Informatics: Practical Guide for Healthcare and Information Technology Professionals (5th ed.). New York.: Lulu.com, 2012, 492p.
  3. EN ISO 12052:2011. Health informatics. Digital imaging and communication in medicine (DICOM) including workflow and data management. Available at: http://iso.org
  4. Oosterwijk Herman. DICOM Basics (3rd ed.). O Tech, 2005, pp. 23–28.
  5. Oosterwijk Herman. PACS Fundamentals (2nd ed.). O Tech, 2004, pp. 25–44.
  6. Dreyer K.J., Hirschorn D.S., Thrall J.H. PACS: A Guide to the Digital Revolution. London.: Springer, 2010, 596p., pp.249–269.

Received 15.12.2015

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ISSUE 179, article 6

DOI: https://doi.org/10.15407/kvt179.01.070

Kibern. vyčisl. teh., 2015, Issue 179, pp 70-80.

Najafian Toomajani Mohamadali, Junior Researcher of Medical Information Systems Department of the International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Glushkov ave., 40, Kiev, 03187, Ukraine, email: Najafian@mail.ru

Budnyk Mykola M., Dr (Engineering), Leading Researcher of Department of Sensor Instruments, Systems and Technologies of Non-Contact Diagnostics of the Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine, Glushkov ave., 40, Kiev, 03187, Ukraine, email: budnyk@meta.ua

Kovalenko Alexander S., Dr (Medicine), Prof., Head of Medical Information Systems Department of the International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Glushkov ave., 40, Kiev, 03187, Ukraine, e-mail: askov49@gmail.com

EVALUATION OF INHOMOGENEITY DEGREE OF ELECTRICAL PROCESSES INTO THE HEART VENTRICLES BASED ON MAGNETOCARDIOGRAPHY

Introduction. Methods for analysis of the current density distribution (CDD) maps as cross-sections of the human heart into the frontal plane were considered. Degrees of non-homogeneity of regional and global kinds are determined based on degree of difference between CDD maps and normal quasi-dipole map. Method for estimation of the abnormality degree of CDD maps caused by failures of electric processes into the heart ventricles has been proposed. Evaluation of regional and global inhomogeneity for each map is determined according to small, medium and large grades.

Purpose of the article is to assess the degree of abnormality of electrical processes in the ventricles of the heart through the MCG mapping, analysis of sets of CDD maps beginning of the QRS complex to the end of the T wave, the calculation of the degree of their differences from normal quasi-dipole map.

Methods. Method of assessing the degree of abnormality of the CDD maps.

Results. In order to achieve reliable classification of the degree of regional (global) maps inhomogeneity is assessed by a 3-point scale (small, medium, large), For a more detailed stratification of patients a total degree of inhomogeneity (abnormality) has developed on a 5-value scale: low (normal), below average (small abnormality), the average (intermediate abnormality), above average (mild abnormality), large (severe abnormality).

First determine the level of regional inhomogeneity. For each map, it is determined according to the 3-value scale – small, mild and severe degree.

Conclusion. This method can be applied to the analysis of not only CDD maps, but also for the magnetic field maps. In this case, it is needed to solve the inverse problem, and instead of the current areas, area of the extreme of the magnetic field were analyzed. However, the magnetic field gives a fairly indirect distribution pattern of excitation into the myocardium, so informative value is much lower for medical analysis.

In addition, method is preferable for using relatively cheap device, which allow make examination under normal condition without magnetically shielded room. Above factor greatly simplifies and reduces the cost of implementation of the MCG technology into clinical practice.

Keywords: current density distribution (CDD) map, estimation of regional inhomogeneity, estimation of global inhomogeneity.

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References

1 Connolly D.C., Elveback L.R., Oxman H.A. Coronary heart disease in residents of Rochester, Minnesota: Prognostic value of the resting electrocardiogram at the time of initial diagnosis of angina pectoris. MayoClin. Proc., 1984; Vol. 59, p. 247–50. https://doi.org/10.1016/S0025-6196(12)61257-9

2 Vinogradova T.S. Akulova F.D., Belotserkovskiy Z.B. et al., Instrumental methods for studing the cardiovascular system. Moscow: Medicine, 1986. 416 p. (in Russian).

3 Lant J., Stroink G., Voorde B. et al. Complementary Nature of Electrocardiografic and Magnetocardiografic Data in Patients with Ischemic Heart Disease. J. Electrocardiology. 1990. V.23, p.315–322. https://doi.org/10.1016/0022-0736(90)90121-H

4 Budnyk M.M., Voytovych I.D., Kozlovsky V.I et al. Diagnostic criteria for chronic ischemic heart disease based on registration and analysis magnitokardiogram. Preprint 2002-5, NAS of Ukraine. Kiev: Glushkov Institute of Cybernetics. 2002, No 5. 49 p. (in Ukrainian).

5 Bakharev A. Ischemia identification, quantification and partial localization in MCG. Int. Patent Application WO 0217769. Cardiomag Imaging Inc., USA, 2002.

6 Kozlovsky V., M. Budnyk, Stadnyuk L., Ryzhenko T. Method of diagnosis of ischemic heart disease. Patent UA 74466. Application No. a 2004 021 170, published 15.12.2005, Bulletin No. 12 (in Ukrainian).

7 Zahrabova A., Budnyk M., Stadnyuk L. et al. Method of estimation of processes of the heart electrical excitation and recovery. Patent UA 13427. Application No. u2006 01007, published 15.03.2006, Bull. No. 3 (in Ukrainian).

8 Chaikovsky I., Budnyk M. Method for estimating abnormality of currents distribution into the heart. Patent UA 83050. Application No. a2006 00 584, published 10.06. 2008,Bulletin No. 11 (in Ukrainian).

9 Chaikovsky I., Budnyk M. Method for estinating abnormality process of ventricular repolarization. Patent UA 83061. Application No. a2006 02821, published 10.06.2008, Bull.No. 11 (in Ukrainian).

10 Wilson at al. The T deflection of the electrocardiogram. Trans. Assoc. Am. Physicians,vol. 46; No. 2, – p. 19–31.

11 Chaikovsky I., Budnyk M., Vasetsky Yu., Najafian M. Method of estimation of the degree of abnormality of electrical processes into the heart ventricles, Patent UA 90701. Application No. a 2007 08 616, published 25.05.2010, Bulletin No. 10 (in Ukrainian).

Received 04.07.2014