Control Systems and Computers, N1, 2022, Article 5

https://doi.org/10.15407/csc.2022.01.044

Control Systems and Computers, 2022, Issue 1 (297), pp. 44-63

UDC 616.12-07 

L.S. FAINZILBERG , D.Sc. (Engineering), Professor,  Chief Researcher, 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, Acad. Glushkova av., 40, Kiev, 03187, Ukraine e-mail: fainzilberg@gmail.com

25 YEARS OF EXPERIENCE IN CREATING AND IMPLEMENTING INTELLECTUAL IT FOR PROCESSING OF COMPLEX FORM BIOMEDICAL SIGNALS

Introduction. An important area of modern information technology application is medical diagnostics, which is based on computer processing of the biomedical signals The purpose of the article is to provide information on the results of basic and applied research that has ensured the practical implementation of the ECG method (fasegraphy method) in various fields of application and to outline further prospects for these studies.

Methods. The technology is based on a stochastic model of generating an artificial signal of complex shape in terms of internal and external distortions.

Results. It is shown that the efficiency in extracting diagnostic information from biomedical signals in conditions of the real distortions, which are not always additive in nature, can be increased by switching from a scalar signal in the time domain to a cognitive image in the phase plane. Original algorithms of adaptive filtering and smoothing have been developed, which made it possible to obtain a numerical estimate of the first derivative of the distorted signal. Recovery of the useful signal (reference cycle) for distorted implementations is carried out by averaging the phase trajectories with the subsequent return to the time domain. 

To increase the reliability of additional diagnostic features of the ECG in the phase space is proposed and clinical data have proven their usefulness in terms of reducing the risk of misdiagnosis. The practical results of the implementation of the diagnostic complex FASEGRAF® have confirmed the effectiveness of fasegraphy in various fields of application. Plans for further prospective research are presented.

Conclusions. Continuation of research allow to create new competitive information technologies and digital medicine devices.

Keywords: information technology, ECG, computational algorithm, signal’s phase trajectories.

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  1. Fainzilberg L.S., 2008. Informatsionnyye tekhnologii obrabotki signalov slozhnoy formy. Teoriya i praktika, Naukova Dumka, Kyiv, 333 p. (In Russian).
  2. Gritsenko V.I., Fainzilberg L. S., 2019. Intellektualnyye informatsionnyye tekhnologii v tsifrovoy meditsine na primere fazagrafii, Kyiv: Naukova Dumka, 423 p. (In Russian).
  3. Pospelov D.A., 1992. “Kognitivnaya grafika – okno v novyy mir”, Programmnyye produkty i sistemy, 2, pp. 4-6. (In Russian).
  4. Fainzilberg L.S., 2017. Osnovy fazagrafii, Osvita Ukrainy, Kyiv, 264 p. (In Russian).
  5. Fainzilberg L.S., 2013. Kompyuternaya diagnostika po fazovomu portretu elektrokardiogrammy, Osvita Ukrainy, Kyiv, 192 p. (In Russian).
  6. Fainzilberg L.S., Potapova T.P., 1995. “Computer Analysis and Recognition of Cognitive Phase Space Electro-Cardio Graphic Image”, Proceeding of the 6th International Conference on Computer Analysis of Images and Patterns CAIP’95, Prague (Czech Republic), pp. 668-673.
    https://doi.org/10.1007/3-540-60268-2_362
  7. Biel L., Pettersson O., Philipson L., Wide P., 2001. “ECG Analysis: a New Approach in Human Identification”, IEEE Trans. on Instrumentation and Measurement, 50 (3), pp. 808-812. DOI: 10.1109/19.930458.
    https://doi.org/10.1109/19.930458
  8. Israel S. A., Irvine J. M., Cheng A., Wiederhold M. D., 2005. “ECG to Identify Individuals”, Pattern Recognition, 38 (1), pp. 133-142. DOI: 10.1016/j.patcog.2004.05.014.
    https://doi.org/10.1016/j.patcog.2004.05.014
  9. Wtibbeler G., Stavridis M., Kreiseler D., Bousseljot R. D., Elster C., 2007. “Verification of Humans using the Electrocardiogram”, Pattern Recognition Letters, 28 (10), pp. 1172-1175. DOI: 10.1016/j.patrec.2007.01.014.
    https://doi.org/10.1016/j.patrec.2007.01.014
  10. Boumbarov O., Velchev Y., Sokolov S., 2009. “ECG Personal Identification in Subspaces using Radial Basis Neural Networks”, IEEE Int. Workshop on Intelligent Data Acquisition and Advanced Computing Systems, pp. 446-451. DOI: 10.1109/IDAACS.2009.5342942.
    https://doi.org/10.1109/IDAACS.2009.5342942
  11. Odinaka I., Lai. P.-H., Kaplan A., O’Sullivan J., Sirevaag E., Kristjansson S., Sheffield A., Rohrbaugh J., 2010. “ECG Biometrics: A Robust Short-time Frequency Analysis”, IEEE International Workshop on Information Forensics and Security, pp. 1-6.
    https://doi.org/10.1109/WIFS.2010.5711466
  12. Poree F., Gallix A., 2011. “Carrault Biometric Identification of Individuals based on the ECG. Which conditions?”, Computing in Cardiology, 38, pp. 761-764.
  13. Noureddine B., Amine N. A., Régis F., Fethi B. R., 2012. “ECG based Human Authentication using Wavelets and Random Forests”, International Journal on Cryptography and Information Security (IJCIS), 2 (2), pp. 1-11.
    https://doi.org/10.5121/ijcis.2012.2201
  14. Singh Y. N., Singh S. K., 2012. “Evaluation of Electrocardiogram for Biometric Authentication”, Journal of Information Security, 3, pp. 39-48. DOI: 10.4236/jis.2012.31005.
    https://doi.org/10.4236/jis.2012.31005
  15. Tseng K-K., Fu L., Liu L., Lee D., Wang C., Li L., 2018. “Human identification with electrocardiogram”, Interprise Information Systems, 12, pp. 798-819. DOI: 10.1080/17517575.2018.1450526.
    https://doi.org/10.1080/17517575.2018.1450526
  16. Hamza S, Yassine BenAyed Y., 2020. “Svm for human identification using the ECG signal”, Procedia Computer Science, 176, pp. 430-439. DOI: 10.1016/j.procs.2020.08.044.
    https://doi.org/10.1016/j.procs.2020.08.044
  17. Fainzilberg L. S., Semergey N. A., 2005. “Individualnyye osobennosti fazovogo portreta EKG kak sredstvo identifikatsii lichnosti”, Proceedings of the 12th International Conference on Automatic Control Automation-2005, Kharkiv, May 30 – June 3, 2005, in 3 volumes, NTU “KhPI”, Kharkiv, 1, pp. 99. (In Russian).
  18. Fainzilberg L. S., Korchynska Z. A., Semerhey M. O., 2015. “Prohramno-tekhnichnyy kompleks dlya doslidzhennya novoho metodu biometrychnoyi identyfikatsiyi osobystosti za fazovym portretom EKG”, Kryminalistychnyy visnyk, 1 (23), pp. 63-71. (In Ukrainian).
  19. Nikitchuk T. M., 2012. “Metod fazovoyi ploshchyny yak sposib doslidzhennya stanu sertsevo-sudynnoyi systemy na osnovi analizu pulsovoyi khvyli”, Visnyk Natsionalnoho tekhnichnoho universytetu Ukrayiny “KPI”, Seriya Radiotekhnika, Radioaparatobuduvannya, 48, pp. 179-185. (In Ukrainian).
  20. Kutsenko L. M., Baltayeva G. Yu., 2014. “Geometrychne modelyuvannya elektrokardiohram z metoyu diahnostyky zakhvoryuvan sertsya”, Suchasni problemy modelyuvannya, 3, pp. 78-86. (In Ukrainian).
  21. Logov A. B., Zamarayev R. Yu., 2016. “Metod informatsionno-fazovykh diagramm dlya otsenki funktsionalnogo sostoyaniya cerdechno-sosudistoy sistemy”, Sistemnyy analiz i upravleniye v biomeditsinskikh sistemakh, 15 (5), pp. 310-314. (In Russian).
  22. Dori G., Denekamp Y., Fishman S., Roisenthal A., Lewis B. S., 2002. “Evaluation of the Phase-plane ECG as Technique for Detecting Acute Coronary Occlusion”, International Journal of Cardiology, 84, pp. 161-170.
    https://doi.org/10.1016/S0167-5273(02)00141-9
  23. Plesnik E., Milenković J., Malgina O., Zajc M., Tasič J. F., 2010. “Določanje Značilk in Klasifikacija Signalov EKG na Osnovi Zaznavanja Točk R v Faznem Prostoru”, Devetnajsta Mednarodna Elektrotehniška in Računalniška Konferenca ERK-2010 (20-22 September 2010, Portorož, Slovenija). Zv. B. S. 323-326.
  24. Fainzilberg L. S., 2012. “Imitatsionnyye modeli porozhdeniya iskusstvennykh elektrokardiogramm v usloviyakh vnutrennikh i vneshnikh vozmushcheniy”, Journal of Qafgaz University. Mathematics and Computer Science, 34, pp. 92-104. (In Russian).
  25. Shilinskayte Z. I., 1965. “Differentsirovaniye elektricheskoy aktivnosti serdtsa”, Kardiologiya, 3, pp. 67-72. (In Russian).
  26. Karamov K. S., Baziyan Zh. A., Alekhin K. P., 1978. “K diagnostike svezhikh ochagovykh porazheniy miokarda”, Kardiologiya, 10, pp. 109-112 (In Russian).
  27. Khalfen E. Sh, Sulkovskaya L. S., 1986. “Klinicheskoye znacheniye issledovaniya skorostnykh pokazateley zubtsa T EKG”, Kardiologiya, 6, pp. 60-62. (In Russian).
  28. Volkova E. G., Kalayev O. F., Kovynev A. R., 1990. “Diagnosticheskiye vozmozhnosti pervoy proizvodnoy EKG v otsenke sostoyaniya koronarnoy arterii u bolnykh ishemicheskoy boleznyu serdtsa”, Terapevticheskiy arkhiv, 3, pp. 35-38. (In Russian).
  29. Akhmetshin A. M., Akhmetshin K. A., 2011. “Informatsionnyye vozmozhnosti ana-liza i otobrazheniya elektrokardiogrammy v bazisakh singulyarnogo razlozheniya vlozhennykh vektorov”, Klinicheskaya informatika i telemeditsina, 7 (8), pp. 58-64. (In Russian).
  30. Fainzilberg L. S., 2008. “Effektivnaya protsedura podavleniya sosredotochennykh garmonicheskikh pomekh pri tsifrovoy obrabotke signalov slozhnoy formy”, Control systems and machines, 4, pp. 49-57, 67 (In Russian).
    https://doi.org/10.1016/j.sysconle.2007.06.015
  31. Fainzilberg L. S., 2002. “Adaptivnoye sglazhivaniye shumov v informatsionnykh tekhnologiyakh obrabotki fiziologicheskikh signalov”, Matematichní mashini í sistemi, 3, pp. 96-104. (In Russian).
  32. Fainzilberg L., Lerche D., 1999. Computer-aided technology of cardio inflammatory disturbance analysis based on phase space cognitive ECG. Final report to the project no 01 KX 96115/1. Transform program. End of project, November. [online] Available at: <http://www.worldcat.org/search?q=no:247734709>.
  33. Fainzilberg L. S., Klubova A. F., Stadnyuk L. A., Chaykovskiy I. A., Lerkhe Ditmar, 2001. “Novyy metod analiza EKG bolnykh revmatoidnym artritom”, Ukraí̈nskiy revmatologíchniy zhurnal, 2, pp. 48-51. (In Russian).
    https://doi.org/10.1023/A:1009046530594
  34. Dyachuk D. D., Grytsenko V. I., Fainzilberg L. S., Kravchenko A. M. et. al., 2017. “Zastosuvannya metodu fazahrafiyi pry provedenni skryninhu ishemichnoyi khvoroby sertsya”, “Methodological recommendations of the Ministry of Health of Ukraine № 163.16/13.17”, Ukrainian Center for Scientific Medical Information and Patent Licensing, Kyiv,32 p. (In Ukrainian).
  35. Fainzilberg L. S., Soroka T. V., 2016. “Mobilnyye prilozheniya dlya virtualnogo vzaimodeystviya vracha i patsiyenta pri distantsionnom monitoringe serdechnoy deyatelnosti”, Kibernetika i vychislitelnaya tekhnika, 184, pp. 8-24 (In Russian).
    https://doi.org/10.15407/kvt184.02.008
  36. Fainzilberg L. S., 2004. “Kompyuternyy analiz i interpretatsiya elektrokardiogramm v fazovom prostranstve”, System Research & Information Technologies, 1, pp. 32-46 (In Russian).
    https://doi.org/10.1615/JAutomatInfScien.v36.i3.50
  37. Grytsenko V. I., Fainzilberg L. S., 2013. “Kompyuternaya diagnostika po signalam slozhnoy formy v usloviyakh vnutrennikh i vneshnikh vozmushcheniy”, Reports of the National Academy of Sciences of Ukraine, 12, pp. 36-44 (In Russian).
  38. Fainzilberg L. S., Minina Ye. N., 2013. “Issledovaniye diagnosticheskoy tsennosti ugla oriyentatsii fazovogo portreta odnokanalnoy EKG kak indikatora funktsionalnogo sostoyaniya miokarda”, Klinicheskaya informatika i telemeditsina, 9 (10), pp. 33-42 (In Russian).
    https://doi.org/10.36691/RJA508
  39. Fainzilberg L. S., Minina Ye. N., 2014. “Otsenka funktsionalnogo sostoyaniya serdechno-sosudistoy sistemy po velichine razbrosa fazovykh trayektoriy odnokanalnoy EKG”, Kibernetika i vychislitelnaya tekhnika, 175, pp. 5-19, 88. (In Russian).
  40. Fainzilberg L. S., 2005. “Novaya informatsionnaya tekhnologiya obrabotki EKG dlya vyyavleniya ishemicheskoy bolezni serdtsa pri massovykh obsledovaniyakh naseleniya”, Control systems and machines, 3, pp. 63-71. (In Russian).
  41. Fainzilberg L. S., 2010. Matematicheskiye metody otsenki poleznosti diagnosticheskikh priznakov, Osvita Ukrainy, Kyiv, 152 p. (In Russian).
  42. Chaykovskyy I. A., Neshcheret O. P., Fainzilberg L. S., Rovenets R. A., Moybenko O. O., 2008. “Doslidzhennya funktsiyi sertsya pry ishemiyi miokarda za dopomohoyu novoho metodu obrobky elektrokardiohramy”, Physiological Journal, 54 (6), pp. 42-48 (In Ukrainian).
  43. Fainzilberg L. S., 2020. “New Approaches to the Analysis and Interpretation of the Shape of Cyclic Signals”, Cybernetics and Systems Analysis, 56 (4), pp. 665-674. DOI: 10.1007/s10559-020-00283-0.
    https://doi.org/10.1007/s10559-020-00283-0
  44. Fainzilberg L., Orikhovska K., Vakhovskyi I., 2017. “Analysis of Subtle Changes in Biomedical Signals Based on Entropy Phase Portrait”, Biomedical Engineering and Electronics, 3, pp. 44-66. DOI: 10.6084/m9.figshare.5230339.
    https://doi.org/10.15407/kvt189.03.005
  45. Fainzilberg L. S., 2021. Sposib diahnostyky ryzyku nayavnosti sertsevo-sudynnoho zakhvoryuvannya za elektrokardiohramoyu, UA, Pat. 122633, Bull. 42. (In Ukrainian).
  46. Fainzilberg L. S., 2014. “Otsenka effektivnosti primeneniya informatsionnoy tekhnologii FAZAGRAF® po dannym nezavisimykh issledovaniy”, Control Systems and Machines, 2, pp. 84-92 (In Russian).
  47. Fainzilberg L. S., 2020. “Intellektualnyye sredstva tsifrovoy meditsiny dlya domashnego primeneniya”, Klinicheskaya informatika i telemeditsina, 15 (16), pp. 45-56. DOI: 10.31071/kit2020.16.03. (In Russian).
    https://doi.org/10.31071/kit2020.16.03
  48. Fainzilberg L. S., 2020. “Expanding of intellectual possibilities of digital tonometers for home using”, Control Systems and Computers, 1, pp. 60-70. DOI: 10.15407/csc.2020.01.060.
    https://doi.org/10.15407/csc.2020.01.060
  49. Fainzilberg L. S., Solovey S. R., 2021. “Self-learning information technology for detecting respiratory disorders in home conditions”, Cybernetics and computer engineering, 2 (204), pp. 64-83. DOI: 10.15407/kvt204.02.064.
    https://doi.org/10.15407/kvt204.02.064

Received  01.02.2022