Comparative Analysis of Single-Objective and Multi-Objective Genetic Algorithms for Blood Pressure Estimation using Cardiovascular Lumped-Parameter Model
DOI:
https://doi.org/10.15282/mekatronika.v7i1.12467Keywords:
Blood pressure, Optimization, Genetic Algorithm , Single-objective Optimization, Multi-Objective Optimization, Lumped Parameter ModelAbstract
Monitoring blood pressure is vital in diagnosing and managing cardiovascular conditions. To support this, computational models that can accurately estimate both systolic (SBP) and diastolic blood pressure (DBP) are becoming increasingly important. However, many optimization methods tend to focus on one value at the expense of the other. This study explores the use of genetic algorithms to fine-tune parameters in a cardiovascular lumped-parameter model. Two approaches are examined: a single-objective genetic algorithm (SOGA) that prioritizes SBP, and a multi-objective genetic algorithm (MOGA) designed to optimize both SBP and DBP simultaneously. Using clinical data from healthy individuals, we assess how well each method replicates real-world measurements. The findings reveal that while SOGA delivers highly accurate SBP estimates, it does so by sacrificing DBP accuracy. MOGA, on the other hand, achieves a more balanced fit, offering reasonable accuracy for both pressure values. The choice between these optimization strategies should therefore be guided by the specific demands of the application. Continuous refinement of multi‐objective formulations and validation on larger datasets will be essential to fully harness the potential of patient‐specific lumped‐parameter models in precision cardiovascular medicine.
References
[1] P. K. Whelton, R. M. Carey, W. S. Aronow, D. E. Casey, K. J. Collins, C. Dennison Himmelfarb, et al., “ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines,” Journal of the American College of Cardiology, vol. 71, no. 19, p. e127-e248, 2018.
[2] G. S. Stergiou, B. Alpert, S. Mieke, R. Asmar, N. Atkins, S. Eckert, et al., “A universal standard for the validation of blood pressure measuring devices: Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) Collaboration Statement,” Hypertension, vol. 71, no. 3, pp. 368-374, 2018.
[3] P. Reymond, Y. Bohraus, F. Perren, F. Lazeyras, and N. Stergiopulos, “Validation of a patient-specific one-dimensional model of the systemic arterial tree,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 301, no. 3, pp. 1173-1182, 2011.
[4] W. N. W. Ab Naim, M. J. M. Mokhtarudin, B. T. Chan, E. Lim, A. A. Bakir, and N. A. N. Mohamed, “The study of myocardial ischemia-reperfusion treatment through computational modelling,” Journal of Theoretical Biology, vol. 509, p. 110527, 2021.
[5] W. N. W. Ab Naim, M. J. M. Mokhtarudin, E. Lim, B. T. Chan, A. Ahmad Bakir, and N. A. N. Mohamed, “The study of border zone formation in ischemic heart using electro‐chemical coupled computational model,” International Journal for Numerical Methods in Biomedical Engineering, vol. 36, no. 11, p. e3398, 2020.
[6] L. F. U. Delestri, A. Al Abed, S. Dokos, M. J. M. Mokhtarudin, F. N. Kok, N. W. Bressloff, et al., “Modelling of cardiac biventricular electromechanics with coronary blood flow to investigate the influence of coronary arterial motion on coronary haemodynamic,” Computer Methods and Programs in Biomedicine, p. 108800, 2025.
[7] S. M. M. Ali, M. F. N. Nashron, W. N. W. Ab Naim, M. J. M. Mokhtarudin, W. El-Bouri, “Haemodynamics in left ventricular remodelling using cardiovascular lumped-parameter model,” Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES), vol. 10, pp. 767-772, 2024.
[8] SMM Ali, W El-Bouri, and M. J. M. Mokhtarudin, “Personalized lumped parameter model for healthy adults using genetic algorithm,” in Annual Congress of the Asia-Pacific Society for Artificial Organs, Singapore: Springer Nature Singapore, pp. 183-194, 2023.
[9] J. P. Mynard and J. J. Smolich, “One-dimensional haemodynamic modeling and wave dynamics in the entire adult circulation,” Annals of Biomedical Engineering, vol. 43, pp. 1443-1460, 2015.
[10] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.
[11] Y. Liang, Z. Chen, G. Liu, and M. Elgendi, “A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China,” Scientific Data, vol. 5, no. 1, pp. 1-7, 2018.
[12] F. Regazzoni and A. Quarteroni, “Accelerating the convergence to a limit cycle in 3D cardiac electromechanical simulations through a data-driven 0D emulator,” Computers in Biology and Medicine, vol. 135, p. 104641, 2021.
[13] H. Li, D. Yuan, X. Ma, D. Cui, and L. Cao, “Genetic algorithm for the optimization of features and neural networks in ECG signals classification,” Scientific Reports, vol. 7, no. 1, p. 41011, 2017.
[14] F. Saeedizadeh and R. K. Moghaddam, “Optimal control of HIV stochastic model through genetic algorithm,” in 2017 7th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 401-405, 2017.
[15] L. Zhong, D. N. Ghista, E. Y. K. Ng, and S. T. Lim, “Passive and active ventricular elastances of the left ventricle,” Biomedical Engineering Online, vol. 4, pp. 1-13, 2005.
[16] A. J. Foy, E. J. Filippone, E. Schaefer, M. Nudy, M. Ruzieh, A. M. Dyer, et al. “Association between baseline diastolic blood pressure and the efficacy of intensive vs standard blood pressure–lowering therapy,” JAMA Network Open, vol. 4, no. 10, pp. e2128980-e2128980 , 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
