ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Selsky A. O., Novikov M. Y., Egorov E. N., Runnova A. E., Kiselev A. R. Identification of characteristics of the response to stress (tilt test) using recurrent analysis in patients with cardiovascular diseases. Izvestiya VUZ. Applied Nonlinear Dynamics, 2026, vol. 34, iss. 4, pp. 588-602. DOI: 10.18500/0869-6632-003226, EDN: EJVGHT

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
PDF icon and_2026-4_selsky_et-al_588-602.pdf
(downloads: 1)
Language: 
Russian
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Article
UDC: 
530.182
DOI: 
10.18500/0869-6632-003226
EDN: 
EJVGHT

Identification of characteristics of the response to stress (tilt test) using recurrent analysis in patients with cardiovascular diseases

Autors: 
Selsky Anton Olegovich, Saratov State Medical University
Novikov Mikhail Yurievich, National Medical Research Center for Therapy and Preventive Medicine
Egorov Evgenij Nikolaevich, Saratov State Medical University
Runnova Anastasia Evgenevna, Saratov State Medical University
Kiselev Anton Robertovich, National Medical Research Center for Therapy and Preventive Medicine
Abstract: 

The purpose of this study is to examine the differences in the response of patients with cardiovascular disease to the passive tilt test stress situation compared to the control group. To explore the potential of this test as an early diagnostic tool and a method for confirming the diagnosis.

Methods. In this study, recurrence indices are calculated to analyze patients’ responses to stress. Pre- and post-tilt test indices will be compared, and joint recurrence indices between these two conditions will be calculated for each patient.

Results. It has been shown that comparing recurrence indices before and after tilt test rotation, depending on the time interval under consideration, allows for the identification of clear differences between patients with various cardiovascular diseases and the control group.

Conclusion. Nonlinear processing of a complex of physiological signals from patients in horizontal and vertical positions allows us to distinguish between sets of recurrent and joint recurrent parameters in groups of patients suffering from arterial hypertension and coronary heart disease, and apparently healthy
volunteers. The analysis revealed differences in the response of patients in different groups to tilt testing, which can be used in the future for the early detection of cardiovascular pathologies and the refinement of diagnostic mechanisms.
 

Key words: 
recurrent analysis
nonlinear dynamics
cardiovascular disease
passive orthostatic test
physiological signals
Acknowledgments: 
This study was supported by grant No 25-44-10019 from the Russian Science Foundation, https://rscf.ru/project/25-44-10019/.
Reference: 
  1. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, Elkind MSV, Evenson KR, Ferguson JF, Gupta DK, Khan SS, Kissela BM, Knutson KL, Lee CD, Lewis TT, Liu J, Loop MSh, Lutsey PL, Ma J, Mackey J, Martin SS, Matchar DB, Mussolino ME, Navaneethan SD, Perak AM, Roth GA, Samad Z, Satou GM, Schroeder EB, Shah SH, Shay ChM, Stokes A, VanWagner LB, Wang N-Y, Tsao CW; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics—2021 Update. Circulation. 2021;143(8):e254–e743. DOI: 10.1161/CIR.0000000000000950.
  2. Cardiovascular diseases (CVDs) [Electronic resource]. World Health Organization. Available from: urlhttps://www.who.int/ru/news-room/fact-sheets/detail/cardiovascular-disea...(cvds).
  3. Borovkova EI, Ishbulatov YM, Hramkov AН, Karavaev AS. Using a mathematical model of cardiovascular system for preparing surrogate data for testing methods of phase synchronization analysis. Izvestiya VUZ. Applied Nonlinear Dynamics. 2021;29(3):356–364. DOI: 10.18500/0869-6632-2021-29-3-356-364.
  4. Hariharan R, Odjidja EN, Scott D, Shivappa N, Hebert JR, Hodge A, de Courten B. The dietary inflammatory index, obesity, type 2 diabetes, and cardiovascular risk factors and diseases. Obesity Reviews. 2022;23(1):e13349. DOI: 10.1111/obr.13349.
  5. de Souza RJ, Mente A, Maroleanu A, Uleryk E, Budylowski P, Schunemann H, Beyene J, Anand SS. Intake of saturated and trans unsaturated fatty acids and risk of all cause mortality, cardiovascular disease, and type 2 diabetes: systematic review and meta-analysis of observational studies. BMJ. 2015;351:h3978. DOI: 10.1136/bmj.h3978.
  6. Strizhakov LA, Babanov SA, Lebedeva MV, Moiseev SV, Fomin VV. Arterial hypertension at the workplace: risk factors and the population value. Ter. Arkh. 2018;90(9):138–143 (in Russian). DOI: 10.26442/terarkh2018909138-143.
  7. Franklin BA, Rusia A, Haskin-Popp C, Tawney A. Chronic stress, exercise and cardiovascular disease: Placing the benefits and risks of physical activity into perspective. Int. J. Environ. Res. Public Health. 2021;18(18):9922. DOI: 10.3390/ijerph18189922.
  8. Lavie CJ, Ozemek C, Carbone S, Katzmarzyk PT, Blair SN. Sedentary behavior, exercise, and cardiovascular health. Circ. Res. 2019;124(5):799–815. DOI: 10.1161/CIRCRESAHA.118.312669.
  9. Huang T, Mariani S, Redline S. Sleep irregularity and risk of cardiovascular events: The multi-ethnic study of atherosclerosis. JACC. 2020;75(9):991–999. DOI: 10.1016/j.jacc.2019.12.054.
  10. Capotosto L, Massoni F, De Sio S, Ricci S, Vitarelli A. Early diagnosis of cardiovascular diseases in workers: Role of standard and advanced echocardiography. Biomed. Res. Int. 2018;2018(1):linebreak 7354691. DOI: 10.1155/2018/7354691.
  11. Noushad S, Ahmed S, Ansari B, Mustafa UH, Saleem Y, Hazrat H. Physiological biomarkers of chronic stress: A systematic review. Int. J. Health Sci. (Qassim). 2021;15(5):46–59.
  12. Dempster KS, Wade TJ, MacNeil AJ, O'Leary DD. Adverse childhood experiences are associated with altered cardiovascular reactivity to head-up tilt in young adults. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2023;324(3):R425–R434. DOI: 10.1152/ajpregu.00148.2022.
  13. Aponte-Becerra L, Novak P. Tilt Test: A Review. J. Clin. Neurophysiol. 2021;38(4):279–286. DOI: 10.1097/WNP.0000000000000625.
  14. Panagopoulou V, Deftereos S, Kossyvakis C, Raisakis K, Giannopoulos G, Bouras G, Pyrgakis V, Cleman MW. NTproBNP: an important biomarker in cardiac diseases. Curr. Top Med. Chem. 2013;13(2):82–94. DOI: 10.2174/1568026611313020002.
  15. Natali A, Vichi S, Landi P, Toschi E , Severi S, L’Abbate A, Ferrannini E. Coronary artery disease and arterial hypertension: clinical, angiographic and follow-up data. Journal of Internal Medicine. 2000;247:219-30. DOI: 10.1046/j.1365-2796.2000.00637.x.
  16. Runnova A, Zhuravlev M, Novikov M, Borodusova A, Kiselev AR. Changes in EEG and ECG signal connectivity during tilt table testing in healthy young adults without syncope. BMC Neurosci. 2025;26:64. DOI: 10.1186/s12868-025-00982-4.
  17. Grubov VV, Runnova AE, Koronovskii AA, Hramov AE. Adaptive filtering of electroencephalogram signals using the empirical-modes method. Tech. Phys. Lett. 2017;43:619–622. DOI: 10.1134/S1063785017070070.
  18. Runnova A, Zhuravlev M, Koronovskii AA, Hramov AE. Mathematical approach to recover EEG brain signals with artifacts by means of Gram-Schmidt transform. In: Saratov Fall Meeting 2016: Laser Physics and Photonics XVII; and Computational Biophysics and Analysis of Biomedical Data II. 2017. P. 254–259. DOI: 10.1117/12.2267828.
  19. Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS. Automated EEG analysis of epilepsy: A review. Knowledge-Based Systems. 2013;45:147–165. DOI: 10.1016/j.knosys.2013.02.014.
  20. Acharya UR, Sree SV, Chattopadhyay S, Yu W, Ang PC. Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 2011;21(3):linebreak 199-211. DOI: 10.1142/S0129065711002808.
  21. Skazkina VV, Mureeva EN, Ishbulatov YuM, Prokhorov MD, Karavaev AS, Hramkov AN, Ezhov DM, Kurbako AV, Panina OS, Chernenkov YuV, Kiselev AR. Cross-recurrence quantification of cardiovascular signals in newborns is a sensitive marker of health status. Russian Open Medical Journal. 2021;13(2):e0203. DOI: 10.15275/rusomj.2024.0203.
  22. Yang YX, Gao ZK, Wang XM, Li Y-L, Han J-W, Marwan N, Kurths J. A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG. Chaos. 2018;28(8):085724. DOI: 10.1063/1.5023857.
  23. Marwan N, Romano MC, Thiel M, Kurths J. Recurrence plots for the analysis of complex systems. Phys. Rep. 2007;438(5–6):237–329. DOI: 10.1016/j.physrep.2006.11.001.
  24. Eckmann JP, Kamphorst SO, Ruelle D. Recurrence plots of dynamical systems. Europhys. Lett. 1987;4(9):973–977. DOI: 10.1209/0295-5075/4/9/004.
  25. Runnova AE, Selskii AO, Emelyanova EP, Fedonnikov AS. The potential possibility of nonlinear recurrence methods application for posttraumatic stress disorder investigation. The Eurasian Journal of Life Sciences. 2025;1(1):17–31. DOI: 10.47093/3033-5493.2025.1.1.17-31.
  26. Sergeev K, Selskii A, Runnova A, Zhuravlev M, Ganilova J, Kiselev A. Changes in EEG recurrence rate during REM sleep caused by Parkinson’s disease. Eur. Phys. J. Spec. Top. 2026. DOI: 10.1140/epjs/s11734-026-02130-3.
  27. Selskii AO, Emelyanova EP, Drozhdeva EE. Markers of patients' condition after orthodontic treatment: application of recurrent analysis to EEG data obtained during cognitive tests. Izvestiya VUZ. Applied Nonlinear Dynamics. 2025;33(3):399–411. DOI: 10.18500/0869-6632-003159.
  28. Marwan N, Kurths J. Line structures in recurrence plots. Phys. Lett. A. 2005;336(4–5):349–357. DOI: 10.1016/j.physleta.2004.12.056.
Received: 
20.04.2026
Accepted: 
07.07.2026
Available online: 
07.07.2026
Published: 
31.07.2026
  • 142 reads