Issue 184, article 2


KVT, 2016, Issue 184, pp.8-25

UDC 681.3.06.14


Fainzilberg L.S.1, Soroka T.V.2

1International Research and Training Center for Information Technologies and Systems of National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Kiev, Ukraine

2National Technical University of Ukraine “Kiev Polytechnical Institute”, Kiev, Ukraine ,

Introduction. The diseases of cardiovascular system lead in the structure of morbidity. The absence of timely treatment leads to severe complications, invalidity and death of the patient. Only preserving medicine can radically change this situation. Fasegraphy is one of perspective directions in cardiology, that allows even by simplified way of ECG registration to detect early signs of disease development.

Purpose. The development of method of fasegraphy for building of complex telemedicine system for mass prophylactic examinations based on client-server architecture and realization of mobile applications for patients and physicians in Android environment is proposed.

Methods. The portable sensor is used for registration of ECG signal, that provides the transfer of digital data through Bluetooth to the patient’s application. The application provides preprocessing of signal, the control of dosed exercise, stress and transfer data to server. The client’s applications are developed in a java programming language version 7 together with Android sdk. The software of server is written in Java version 8 in conjunction with frameworks Spring 4.3 for REST API and Hibernate 5.1 as ORM. The database is based on MySql 5.5.

Results. The server software provides automatic selection of ECG with atypical cycles for which the physician must pay attention primarily. The algorithm of selection is based on the analysis of ordered Hausdorff distances between trajectories of cycles on the phase plane. When the information about detection of atypical cycles is received, the physician may view ECG, perform additional analysis those ECGs and send appropriate recommendations to patient.

Conclusions. Client-server organization of interaction of physician and patient increases the effectiveness of screening surveys and optimizing time spent by a doctor on the medical services to their patients.

Keywords: client-server system, fasegraphy, distant monitoring, atypical cycles of ECG.

Download full text (ru)!


  1. Cassar A., Holmes D.R., Rihal C.S. Chronic coronary artery disease: diagnosis and management. Mayo Clin. Proc., 2009, Vol. 84, № 12, pp. 1130–1146.
  2. Basso C., Corrado D., Thiene G. Prevention of sudden cardiac death in the young and in athletes: dream or reality? Cardiovascular Pathology. 2010, Vol. 19, № 4, pp. 207–217.
  3. Thaulow E., Erikssen J., Sandvik L. Initial clinical presentation of cardiac disease in asymptomatic men with silent myocardial ischemia and angiographically documented coronary artery disease (the Oslo Ischemia Study). American Journal of Cardiology, 1993, Vol. 72, № 9, pp. 629–633.
  4. Gozhenko A., Kulbіda M., Kochetov A. Preventive medical science strategy — a way to improve the efficiency of health care. Journal of National Academy of Sciences of Ukraine, 2011, № 12, pp. 64–69 (in Ukrainian).
  5. Solopov V., Sadykova A., Fedoseyeva T. Restrictions of automatic analysis of computer. Kazansky Medical Journal, 2012, Т.93, № 4, pp. 687-691 (in Russian).
  6. Lourenço A. Unveiling the Biometrical Potential of Finger-Based ECG Signals. Computational Intelligence and Neuroscience, 2011, Vol. 2011, pp. 1–8.
  7. Grier J.W. Comparison and review of portable, handheld, 1-lead/channel ECG / EKG recorders. Available at
  8. Fainzilberg L.S. Computer diagnosis of the phase portrait of the electrocardiogram. Kiev: Osvita Ukrainy, 2013, 191 p. (in Russian).
  9. Fainzilberg L.S. ECG Averaging based on Hausdorff Metric. International Journal of Biomagnetism, 2003, Vol. 5, № 1, pp. 236–237.
  10. Fainzilberg L.S. Nowa metoda interpretacji zapisu EKG w balaniach skriningowych oraz w opiece domowej. Zdrowie publiczne (Public Health), 2005, Vol. 115, № 4, pp.458–464.
  11. Matjaˇz Perc. Nonlinear time series analysis of the human electrocardiogram. European Journal of Physics, 2005, № 26, pp. 757–768.
  12. Minina E.H., Fainzilberg L.S. Analysis of the functional condition of the cardiovascular system of the combination of features of the phase portrait of single-channel ECG. Russian Cardiology Journal, 2015, № 12 (128), pp. 7–13 (in Russian).
  13. Fainzilberg L.S. Information signal processing technology complex shape. Theory and practice. Kiev: Scientific Thought, 2008, 333 p. (in Russian).
  14. Gritsenko V.Y., Fainzilberg L.S. Information technology of FAZAGRAF® for the integrated estimation of the cardiovascular’s system condition on phase portrait of electrocardiogram. Doctor and Information Technology, 2013, № 3, pp.52–63 (in Russian).
  15. Vishnevsky V.V. Grid-based system for mass storing and and processing of digital electrocardiograms. Ukrainian Journal of Telemedicine and Medical Telematics, 2013, E. 11, № 1, pp. 202–208 (in Russian).
  16. Fainzilberg L.S., Soroka T.V. The development of telemedicine systems for remote monitoring heart activity based on the method of fasegraphy. East European Journal of advanced technologies, 2015, № 6/9(78), pp. 37–46 (in Russian).
  17. Kaya Y., Pehlivan H. Classification of Premature Ventricular Contraction in ECG. International Journal of Advanced Computer Science and Applications, 2015, Vol. 6. № 7, pp. 34–40.
  18. Goldberger A.L. Fractal mechanisms in the electrophysiology of the heart. IEEE Eng. Med. Biol., 1992, № 11, pp. 47–52.
  19. Genkin A.A. The new information technology of medical data analysis. SPB: Polytechnic, 1999, 192 p. (in Russian).

Received 28.04.16