004.652

ontology, integrated onto-oriented information environment, simulation, processing methods, cyclic signals.

The article gives the reasoning to the relevance of developing a generalized architecture of integrated onto-oriented information environment for simulation and processing of cyclic signals based on the theory of cyclic functional relations, as well as formulates the general requirements to it and its developingt. The research deals with statement of and creating the generalized architectures of the components of the integrated onto-oriented information environment for simulation and processing of cyclic signals, namely, for information-oriented reference system in the field of simulation and processing of cyclic signals; knowledge base of the integrated information environment, the core of which is the corresponding ontology; onto-oriented expert decision support system in the field of simulation and processing of cyclic signals; information system with onto-oriented architecture for simulation and processing of cyclic signals.

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2. Gardner W., Archer T. Exploitation of cyclostationarity for identifying the Volterra kernels of non–linear systems. IEEE Transactions on Information Theory. 1993. Nо. 39 (2). P. 535–542.
3. Gardner W., Brown W. Fraction of time probability for time-series that exhibit cyclostationarity. Signal Processing. 1991. Vol. 23. P. 273–292.
4. Israa Shaker Tawfic, Sema Koc Kayhan. (2017) Improving recovery of ECG signal with deterministic guarantees using split signal for multiple supports of matching pursuit (SSMSMP) algorithm, Computer Methods and Programs in Biomedicine. Vol. 139. 2017. P. 39–50. DOI: org/10.1016/j.cmpb.2016.10.014.
5. Fumagalli F., Silver A. E., Tan Q., Zaidi N., Ristagno G. (2018), Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast Reconfirmation technology, Heart Rhythm, 15 (2). P. 248–255. DOI: 10.1016/j.hrthm.2017.09.003.
6. Napoli N. J., Demas M. W., Mendu S., Stephens C. L., Kennedy K. D, Harrivel A. R, Bailey R. E., Barnes L.E. (2018), Uncertainty in heart rate complexity metrics caused by R-peak perturbations, Computers in Biology and Medicine. 103. P. 198–207. DOI: 10.1016/j.compbiomed.2018.10.009.
7. Napolitano A. (2016) Cyclostationarity: Limits and generalizations. Signal Processing. Vol. 120. March 2016. P. 323–347.
8. Lepage R., Boucher J., Blan J. and Cornilly J., “ECG segmentation and p-wave feature extraction: application to patients prone to atrial fibrillation”, IEEE EMBS 2001; 1:298–301.
9. Moody B. G. and Mark R. G., “A new method for detecting atrial fibrillation using R-R intervals”, IEEE Computers in Cardiology 1983; 10:227-230.
10. Cerutti S., Mainardi L. T., Porta A. and Bianchi A. M., “Analysis of the Dynamics of RR Interval Series for the Detection of Atrial Fibrillation Episodes”, IEEE Computers in Cardiology 1997; 24:77-80.
11. Tateno K. and Glass L., “A Method for Detection of Atrial Fibrillation Using RR intervals”, IEEE Computers in Cardiology 2000; 27:391-394.
12. Karyotis V., Khouzani M. H. R. (2016) Malware Diffusion Models for Modern Complex Networks. Theory and Applications. USA. 2016. P. 324. ISBN 978-0-12-802714-1.
13. Sericola B. (2013) Markov Chains: Theory and Applications. London, July 2013. P. 416. ISBN: 978-1-848-21493-4.
14. Shaffer F., Ginsberg J. P. (2017), An Overview of Heart Rate Variability Metrics and Norms, Frontiers in Public Health. Volume 5. Article 258. September 2017. P. 1–17. DOI: 10.3389/fpubh.2017.00258.
15. Berkaya S. K., Uysal A. K., Gunal E. S, Ergin S., Gunal S., Gulmezoglu M. B. (2018), A survey on ECG analysis. Biomedical Signal Processing and Control, 43, 216–235.
16. Shen C., Yu Z., Liu Z. (2015), The use of statistics in heart rhythm research: a review, Heart Rhythm, 12 (6), 1376–1386.
17. Peter Olofsson, Mikael Andersson. Probability, Statistics, and Stochastic Processes. John Wiley & Sons, INC, 2012. USA. P. 553.
18. Athanasios Christou Micheas. Theory of Stochastic Objects Probability, Stochastic Processes and Inference. Chapman and Hall/CRC. January 24. 2018. P. 408. ISBN: 9781466515215.
19. Hisashi Kobayashi, Brian L. Mark, William Turin. Probability, Random Processes, and Statistical Analysis. USA by Cambridge University Press, New York 2012. –p.812. Hardback 978-0-521-89544-6.
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22. Ben Salah R., Hadidi T. and Chabchoub S., “Intelligent diagnosis method of cardiovascular anomalies using medical signal processing,” 2015 World Congress on Information Technology and Computer Applications (WCITCA), Hammamet, 2015, P. 1–5. DOI: 10.1109/WCITCA.2015.7367032.
23. Rahimpour M., Asl M. E. and Merati M. R., “ECG fiducial points extraction using QRS morphology and adaptive windowing for real-time ECG signal analysis,” 2016 24th Iranian Conference on Electrical Engineering (ICEE). Shiraz. 2016. P. 1925–1930. DOI: 10.1109/IranianCEE.2016.7585836.
24. Ciucurel C., Georgescu L., Iconaru E. I. (2018), ECG response to submaximal exercise from the perspective of Golden Ratio harmonic rhythm, Biomedical Signal Processing and Control, 40, 156–162.
25. Onyskiv P., Lupenko S., Lytvynenko I., Zozulia A. Mathematical modeling and processing of high resolution rhythmocardio signal based on a vector of stationary and stationary related random sequences. IDDM’2020: 3rd International Conference on Informatics & Data-Driven Medicine, November 19–21, 2020, Växjö, Sweden. CEUR Workshop Proceedings, 2020, 2753, pp. 149–155. URL: http://ceur-ws.org/Vol-2753/short8.pdf.
26. Gorkunenko A. B., Lupenko S. A. Obg'runtuvannja diagnostychnyh i prognostychnyh oznak v informacijnyh systemah analizu ta prognozuvannja cyklichnyh ekonomichnyh procesiv. Naukovyj visnyk NLTU Ukrainy: zbirnyk naukovo-tehnichnyh prac. L'viv. 2012. No. 22.9. P. 347−352. [In Ukrainian].
27. Lupenko S., Lytvynenko I., Stadnyk N. Method for reducing the computational complexity of processing discrete cyclic random processes in digital data analysis systems Scientific Journal of the Ternopil national technical university. 2020. Vol. 97. No. 1. P. 110–121. URL: https://doi.org/10.33108/visnyk_ tntu.
28. Lupenko S., Lytvynenko I., Stadnyk N., Zozulia A. та інші Mathematical model of rhythmocardiosignal in vector view of stationary and stationary-related case sequences. Advanced Information Systems. National Technical University “Kharkiv Polytechnic Institute”. 2020. Vol. 4. No. 2. P. 42–46. DOI: 10.20998/2522-9052.2020.2.08.
29. Hutsaylyuk V., Lytvynenko I., Maruschak P., Schnell, G. та інші.A new method for modeling the cyclic structure of the surface microrelief of titanium alloy ti6al4v after processing with femtosecond pulses. Materials, 2020, 13(21), pp. 1–8, 4983. DOI: 10.3390/ma13214983.
30. Marushak P. O., Lytvynenko I. O., Lupenko S. A., Popovych P. V. Modeling of the Ordered Surface Topography of Statically Deformed Aluminum Alloy. Materials Science. 2016. Vol. 52. No. 1. P. 113–122.

1. Gardner W., Napolitano A., L. Paura Cyclostationarity: Half a century of research. Signal Processing. 2005. Vol. 86. Р. 639–697.
2. Gardner W., Archer T. Exploitation of cyclostationarity for identifying the Volterra kernels of non–linear systems. IEEE Transactions on Information Theory. 1993. Nо. 39 (2). P. 535–542.
3. Gardner W., Brown W. Fraction of time probability for time-series that exhibit cyclostationarity. Signal Processing. 1991. Vol. 23. P. 273–292.
4. Israa Shaker Tawfic, Sema Koc Kayhan. (2017) Improving recovery of ECG signal with deterministic guarantees using split signal for multiple supports of matching pursuit (SSMSMP) algorithm, Computer Methods and Programs in Biomedicine. Vol. 139. 2017. P. 39–50. DOI: org/10.1016/j.cmpb.2016.10.014.
5. Fumagalli F., Silver A. E., Tan Q., Zaidi N., Ristagno G. (2018), Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast Reconfirmation technology, Heart Rhythm, 15 (2). P. 248–255. DOI: 10.1016/j.hrthm.2017.09.003.
6. Napoli N. J., Demas M. W., Mendu S., Stephens C. L., Kennedy K. D, Harrivel A. R, Bailey R. E., Barnes L.E. (2018), Uncertainty in heart rate complexity metrics caused by R-peak perturbations, Computers in Biology and Medicine. 103. P. 198–207. DOI: 10.1016/j.compbiomed.2018.10.009.
7. Napolitano A. (2016) Cyclostationarity: Limits and generalizations. Signal Processing. Vol. 120. March 2016. P. 323–347.
8. Lepage R., Boucher J., Blan J. and Cornilly J., “ECG segmentation and p-wave feature extraction: application to patients prone to atrial fibrillation”, IEEE EMBS 2001; 1:298–301.
9. Moody B. G. and Mark R. G., “A new method for detecting atrial fibrillation using R-R intervals”, IEEE Computers in Cardiology 1983; 10:227-230.
10. Cerutti S., Mainardi L. T., Porta A. and Bianchi A. M., “Analysis of the Dynamics of RR Interval Series for the Detection of Atrial Fibrillation Episodes”, IEEE Computers in Cardiology 1997; 24:77-80.
11. Tateno K. and Glass L., “A Method for Detection of Atrial Fibrillation Using RR intervals”, IEEE Computers in Cardiology 2000; 27:391-394.
12. Karyotis V., Khouzani M. H. R. (2016) Malware Diffusion Models for Modern Complex Networks. Theory and Applications. USA. 2016. P. 324. ISBN 978-0-12-802714-1.
13. Sericola B. (2013) Markov Chains: Theory and Applications. London, July 2013. P. 416. ISBN: 978-1-848-21493-4.
14. Shaffer F., Ginsberg J. P. (2017), An Overview of Heart Rate Variability Metrics and Norms, Frontiers in Public Health. Volume 5. Article 258. September 2017. P. 1–17. DOI: 10.3389/fpubh.2017.00258.
15. Berkaya S. K., Uysal A. K., Gunal E. S, Ergin S., Gunal S., Gulmezoglu M. B. (2018), A survey on ECG analysis. Biomedical Signal Processing and Control, 43, 216–235.
16. Shen C., Yu Z., Liu Z. (2015), The use of statistics in heart rhythm research: a review, Heart Rhythm, 12 (6), 1376–1386.
17. Peter Olofsson, Mikael Andersson. Probability, Statistics, and Stochastic Processes. John Wiley & Sons, INC, 2012. USA. P. 553.
18. Athanasios Christou Micheas. Theory of Stochastic Objects Probability, Stochastic Processes and Inference. Chapman and Hall/CRC. January 24. 2018. P. 408. ISBN: 9781466515215.
19. Hisashi Kobayashi, Brian L. Mark, William Turin. Probability, Random Processes, and Statistical Analysis. USA by Cambridge University Press, New York 2012. –p.812. Hardback 978-0-521-89544-6.
20. Oliver C. Ibe. Fundamentals of Applied Probability and Random Processes. 2nd Edition. Academic Press is an imprint of Elsevier. USA 2014. P. 431.
21. Scott Miller, Donald Childers Probability and Random Processes: With Applications to Signal Processing and Communications. 2nd Edition. Academic Press is an imprint of Elsevier. USA 2012. P.593.
22. Ben Salah R., Hadidi T. and Chabchoub S., “Intelligent diagnosis method of cardiovascular anomalies using medical signal processing,” 2015 World Congress on Information Technology and Computer Applications (WCITCA), Hammamet, 2015, P. 1–5. DOI: 10.1109/WCITCA.2015.7367032.
23. Rahimpour M., Asl M. E. and Merati M. R., “ECG fiducial points extraction using QRS morphology and adaptive windowing for real-time ECG signal analysis,” 2016 24th Iranian Conference on Electrical Engineering (ICEE). Shiraz. 2016. P. 1925–1930. DOI: 10.1109/IranianCEE.2016.7585836.
24. Ciucurel C., Georgescu L., Iconaru E. I. (2018), ECG response to submaximal exercise from the perspective of Golden Ratio harmonic rhythm, Biomedical Signal Processing and Control, 40, 156–162.
25. Onyskiv P., Lupenko S., Lytvynenko I., Zozulia A. Mathematical modeling and processing of high resolution rhythmocardio signal based on a vector of stationary and stationary related random sequences. IDDM’2020: 3rd International Conference on Informatics & Data-Driven Medicine, November 19–21, 2020, Växjö, Sweden. CEUR Workshop Proceedings, 2020, 2753, pp. 149–155. URL: http://ceur-ws.org/Vol-2753/short8.pdf.
26. Gorkunenko A. B., Lupenko S. A. Obg'runtuvannja diagnostychnyh i prognostychnyh oznak v informacijnyh systemah analizu ta prognozuvannja cyklichnyh ekonomichnyh procesiv. Naukovyj visnyk NLTU Ukrainy: zbirnyk naukovo-tehnichnyh prac. L'viv. 2012. No. 22.9. P. 347−352. [In Ukrainian].
27. Lupenko S., Lytvynenko I., Stadnyk N. Method for reducing the computational complexity of processing discrete cyclic random processes in digital data analysis systems Scientific Journal of the Ternopil national technical university. 2020. Vol. 97. No. 1. P. 110–121. URL: https://doi.org/10.33108/visnyk_ tntu.
28. Lupenko S., Lytvynenko I., Stadnyk N., Zozulia A. та інші Mathematical model of rhythmocardiosignal in vector view of stationary and stationary-related case sequences. Advanced Information Systems. National Technical University “Kharkiv Polytechnic Institute”. 2020. Vol. 4. No. 2. P. 42–46. DOI: 10.20998/2522-9052.2020.2.08.
29. Hutsaylyuk V., Lytvynenko I., Maruschak P., Schnell, G. та інші.A new method for modeling the cyclic structure of the surface microrelief of titanium alloy ti6al4v after processing with femtosecond pulses. Materials, 2020, 13(21), pp. 1–8, 4983. DOI: 10.3390/ma13214983.
30. Marushak P. O., Lytvynenko I. O., Lupenko S. A., Popovych P. V. Modeling of the Ordered Surface Topography of Statically Deformed Aluminum Alloy. Materials Science. 2016. Vol. 52. No. 1. P. 113–122.