Bifurcation and chaos in N-type and S-type muscular blood vessel models

Wenxin Zhang , Lijun Pei

Electronic Research Archive ›› 2025, Vol. 33 ›› Issue (3) : 1285 -1305.

PDF (13719KB)
Electronic Research Archive ›› 2025, Vol. 33 ›› Issue (3) :1285 -1305. DOI: 10.3934/era.2025057
Research article
research-article
Bifurcation and chaos in N-type and S-type muscular blood vessel models
Author information +
History +
PDF (13719KB)

Abstract

In this paper, we have constructed two muscular blood vessel systems influenced by external disturbances by using the non-autonomous delayed differential equation (DDE). As an important physiological structure of the human body, muscle blood vessels participate in many activities such as blood flow. Many diseases are associated with abnormal dynamics of muscle blood vessels. From a mathematical point of view, vasospasm is caused by a chaotic state of blood vessels. Vasospasm is the manifestation of this disease in the vascular system, which can cause blood vessel blockage and even harm human health when it is serious. We conducted the dynamical analysis of the S- type and N-type muscular blood vessel systems by utilizing bifurcation diagrams, time histories, and pseudo-phase portraits, and investigated the effects of different parameters on these systems. Specifically, when parameters change, rich dynamical phenomena occur, such as equilibria, periodic solutions, quasi-periodic solutions, Hopf bifurcation, and chaos, as well as the route of period-doubling bifurcation to chaos. Meanwhile, we analyzed approximate analytical solutions by using the method of multiple scales (MMS) and determined the stability of steady-state solutions through the Routh- Hurwitz criterion. The results indicate that the MMS can deduce better analytical results for some non-autonomous DDEs.

Keywords

muscular blood vessel systems / chaos / MMS / non-autonomous DDE / Hopf bifurcation

Cite this article

Download citation ▾
Wenxin Zhang, Lijun Pei. Bifurcation and chaos in N-type and S-type muscular blood vessel models. Electronic Research Archive, 2025, 33 (3) : 1285-1305 DOI:10.3934/era.2025057

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

C. Sorensen, J. Hess, Treatment and prevention of heat-related illness, N. Engl. J. Med., 387 (2022), 1404-1413. https://doi.org/10.1056/NEJMcp2210623

[2]

N. Gonzalez-Jaramillo, M. Wilhelm, A. M. Arango-Rivas, V. Gonzalez-Jaramillo, C. Mesa-Vieira, B. Minder, et al., Systematic review of physical activity trajectories and mortality in patients with coronary artery disease, J. Am. Coll. Cardiol., 79 (2022), 1690-1700. https://doi.org/10.1016/j.jacc.2022.02.036

[3]

F. Alfonso, C. Fernández-Pérez, N. del Prado, M. García-Guimaraes, J. L. Bernal, T. Bastante, et al., Primary percutaneous coronary intervention in patients with spontaneous coronary artery dissection vs coronary artery disease, JACC: Cardiovasc. Interventions, 16 (2023), 1860-1869. https://doi.org/10.1016/j.jcin.2023.05.044

[4]

M. T. Nouei, A. V. Kamyad, M. Sarzaeem, S. Ghazalbash, Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro-fuzzy inference system and K-means clustering, Expert Syst., 33 (2016), 230-238. https://doi.org/10.1111/exsy.12145

[5]

V. Auffret, C. Ridard, N. Salerno, S. Sorrentino, Unmet needs in TAVR: conduction disturbances and management of coronary artery disease, J. Clin. Med. Res., 11 (2022), 6256. https://doi.org/10.3390/jcm11216256

[6]

X. Y. Zhang, H. W. Du, G. Song, F. X. Bao, Y. F. Zhang, W. Wu, et al., X-ray coronary centerline extraction based on C-UNet and a multifactor reconnection algorithm, Comput. Methods Programs Biomed., 226 (2022), 107114. https://doi.org/10.1016/j.cmpb.2022.107114

[7]

D. D. Hao, S. Ding, L. W. Qiu, Y. S. Lv, B. W. Fei, Y. Q. Zhu, et al., Sequential vessel segmentation via deep channel attention network, Neural Networks, 128 (2020), 172-187. https://doi.org/10.1016/j.neunet.2020.05.005

[8]

A. Kerkeni, A. Benabdallah, A. Manzanera, M. H. Bedoui, A coronary artery segmentation method based on multiscale analysis and region growing, Comput. Med. Imaging Graphics, 48 (2016), 49-61. https://doi.org/10.1016/j.compmedimag.2015.12.004

[9]

S. Yang, J. Kweon, J. H. Roh, J. H. Lee, H. Kang, L. J. Park, et al., Deep learning segmentation of major vessels in X-ray coronary angiography, Sci. Rep., 9 (2019), 1-11. https://doi.org/10.1038/s41598-019-53254-7

[10]

X. J. Liu, S. Li, B. Wang, L. Xu, Z. F. Gao, G. Yang, Motion estimation based on projective information disentanglement for 3D reconstruction of rotational coronary angiography, Comput. Biol. Med., 157 (2023), 106743. https://doi.org/10.1016/j.compbiomed.2023.106743

[11]

D. M. Bappy, A. Hong, E. Choi, J. O. Park, C. S. Kim, Automated three-dimensional vessel reconstruction based on deep segmentation and bi-plane angiographic projections, Comput. Med. Imaging Graphics, 92 (2021), 101956. https://doi.org/10.1016/j.compmedimag.2021.101956

[12]

M. Hwang, S. B. Hwang, H. Yu, J. Kim, D. Kim, W. Hong, et al., A simple method for automatic 3D reconstruction of coronary arteries from X-Ray angiography, Front. Physiol., 12 (2021), 724216. https://doi.org/10.3389/fphys.2021.724216

[13]

H. Y. Yu, H. B. Wang, J. Y. Chang, W. J. Liu, F. H. Wang, J. Y. Niu, Design and evaluation of vascular interventional robot system for complex coronary artery lesions, Med. Biol. Eng. Comput., 61 (2023), 1-16. https://doi.org/10.1007/s11517-023-02775-5

[14]

H. J. Li, Y. L. Zhu, L. Jing, W. Ying, Consensus of second-order delayed nonlinear multi-agent systems via node-based distributed adaptive completely intermittent protocols, Appl. Math. Comput., 326 (2018), 1-15.

[15]

H. J. Li, J. J. Zhang, L. Jing, W. Ying, Neural-network-based adaptive quasi-consensus of nonlinear multi-agent systems with communication constrains and switching topologies, Nonlinear Anal. Hybrid Syst., 35 (2020), 100833.

[16]

S. B. He, H. Natiq, S. Mukherjee, Multistability and chaos in a noise-induced blood flow, Eur. Phys. J. Spec. Top., 230 (2021), 1525-1533. https://doi.org/10.1140/epjs/s11734-021-00032-0

[17]

S. B. He, N. A. A. Fataf, S. Banerjee, K. H. Sun, Complexity in the muscular blood vessel model with variable fractional derivative and external disturbances, Physica A, 526 (2019), 120904. https://doi.org/10.1016/j.physa.2019.04.140

[18]

C. J. Lin, S. K. Yang, H. T. Yau, Chaos suppression control of a coronary artery system with uncertainties by using variable structure control, Comput. Math. Appl., 64 (2012), 988-995. https://doi.org/10.1016/j.camwa.2012.03.007

[19]

C. Y. Gong, Y. M. Li, X. H. Sun, Backstepping control of synchronization for biomathematical model of muscular blood vessel, J. Appl. Sci., 24 (2006), 604-607.

[20]

P. P. Singh, M. Borah, A. Datta, S. Jafari, B. K. Roy, Integer cum fractional ordered active-adaptive synchronization to control vasospasm in chaotic blood vessels to reduce risk of COVID-19 infections, Int. J. Comput. Math., 102 (2025), 14-28. https://doi.org/10.1080/00207160.2022.2163167

[21]

S. H. Gong, M. A. Han, An estimate of the number of limit cycles bifurcating from a planar integrable system, Bull. Sci. Math., 176 (2022), 103118.

[22]

J. Guo, Z. S. Zhao, F. D. Shi, R. K. Wang, S. S. Li, Observer-Based synchronization control for coronary artery time-delay chaotic system, IEEE Access, 7 (2019), 51222-51235. https://doi.org/10.1109/ACCESS.2019.2909749

[23]

H. Ding, D. W. Qian, S. Lee, L. L. Zhu, Sliding-mode-based chaos suppression of coronary artery systems, Meas. Control, 56 (2023). https://doi.org/10.1177/00202940231180842

[24]

C. Chantawat, T. Botmart, Finite-time H∞ synchronization control for coronary artery chaos system with input and state time-varying delays , PLoS One, 17 (2022). https://doi.org/10.1371/journal.pone.0266706

[25]

D. W. Qian, H. Ding, S. Lee, H. Bae, Suppression of chaotic behaviors in a complex biological system by disturbance observer-based derivative-integral terminal sliding mode, J. Autom. Sin., 7 (2020), 126-135. https://doi.org/10.1109/JAS.2019.1911834

[26]

A. Ranjbar, A. Madady, M. Ramezani, A. Khosravi, Synchronization of stochastic fractional-order model of muscular blood vessels, J. Vib. Control, 30 (2024), 3775-3783. https://doi.org/10.1177/10775463231201732

[27]

J. Guo, Z. S. Zhao, J. Zhang, G. Ding, Adaptive observation control for synchronization of coronary artery time-delay systems, Mod. Phys. Lett. B, 33 (2019), 1950454. https://doi.org/10.1142/S0217984919504542

[28]

C. H. Yang, H. Ma, K. C. Wang, Self-tuning integral-type finite-time-stabilized sliding mode control for state synchronization of coronary artery systems, Sens. Mater., 34 (2022), 3869-3881. https://doi.org/10.18494/SAM4022

[29]

R. K. Wang, B. H. Li, Z. S. Zhao, J. Guo, Z. Y. Zhu, Synchronization of fuzzy control design based on Bessel-Legendre inequality for coronary artery state time-delay system, IEEE Access, 7 (2019), 181933-181941. https://doi.org/10.1109/ACCESS.2019.2957500

[30]

G. El Khaloufi, N. Chaibi, S. B. Alaoui, I. Boumhidi, Generalized dissipativity dynamic output feedback control for coronary artery system, Trans. Inst. Meas. Control, 46 (2024), 716-728. https://doi.org/10.1177/01423312231182

[31]

Z. S. Zhao, X. M. Li, J. Zhang, Y. Z. Pei, Terminal sliding mode control with self-tuning for coronary artery system synchronization, Int. J. Biomath., 10 (2017). https://doi.org/10.1142/S1793524517500413

[32]

H. Yang, Multiscale recurrence quantification analysis of spatial cardiac vectorcardiogram signals, IEEE Trans. Biomed. Eng., 58 (2011), 339-347. https://doi.org/10.1109/TBME.2010.2063704

[33]

Y. Chen, H. Yang, Multiscale recurrence analysis of long-term nonlinear and nonstationary time series, Chaos, Solitons & Fractals, 45 (2012), 978-987. https://doi.org/10.1016/j.chaos.2012.03.013

[34]

Z. Y. Xu, Z. R. Liu, The chaos phenomenon of the models for two types of muscular blood vessel, J. Biomath., 1 (1986), 109-115.

PDF (13719KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/

〈 〉