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Abstract

Maintaining optimal Blood Pressure (BP) is vital, as abnormal BP levels pose substantial challenges to patient recovery in post-operative care. The manual administration of Sodium Nitroprusside (SNP) is a common approach to lower BP by relaxing peripheral vascular smooth muscles. Nevertheless, because of the inconsistency in drug sensitivity between patients, manual dosing is inaccurate and labour-intensive as it necessitates continuous expert monitoring. Therefore, this research adapts a control method to regulate BP in post-operative patients with hypertension. The Prairie Dog Optimization-based Proportional-Integral-Derivative (PDO-PID) controller adapts in real-time to the particular physiological responses of the patients, assuring precise and individualized SNP dosing. According to simulation results, the controller effectively controls BP levels over an extended time, generating an execution time of 63.613s and a reduced settling time of 1.05s. Corresponding SNP infusion levels are also effectively regulated, which is significantly smaller than the previous control approaches.

Keywords

Blood pressure Sodium nitroprusside Prairie dog optimization Proportional-integral-derivative controller Infusion pump

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Anbazhagan, J., Kar , S. ., Krishna Prakash Arunachalam, & Koithyar, A. . (2025). Optimized PID control for automated blood pressure management in post-operative care. Future Technology, 4(3), 10–18. Retrieved from https://fupubco.com/futech/article/view/294
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References

  1. T. E. Baum, E. Adam, C. S. Guay, G. Schamberg, M. Kazemi, T. Heldt, and E. N. Brown,“Dynamic Estimation of Cardiovascular State From Arterial Blood Pressure Recordings," in IEEE Transactions on Biomedical Engineering,” vol. 71, no. 11, pp. 3146-3159, 2024. DOI: https://doi.org/10.1109/TBME.2024.3408808.
  2. M. Liaqat, M. G. Khan, M. R. Fazal, Y. Ghadi and M. Adnan, “Multi-Criteria Storage Selection Model for Grid-Connected Photovoltaics Systems,” in IEEE Access, vol. 9, pp. 115506-115522, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3105592
  3. S. Chen, T. Reheman, C. Li, X. Wu, H. Lin, Z. Lin, X. Liang, H. Li, and J. Zhang,“Hypertension Monitoring by a Real Time Management System for Patients in Community and Its Data Mining by Vector Autoregressive Model,” in IEEE Access, vol. 11, pp. 12607-12622, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3240084.
  4. M. A. Sultan and W. Saadeh, “Continuous Patient-Independent Estimation of Respiratory Rate and Blood Pressure Using Robust Spectro-Temporal Features Derived From Photoplethysmogram Only,” in IEEE Open Journal of Engineering in Medicine and Biology, vol. 5, pp. 637-649, 2024. DOI: https://doi.org/10.1109/OJEMB.2023.3329728.
  5. Q. Lin, T. Li, P. M. Shakeel, and R. D. J. Samuel,“Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management,”Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp.3329-3340, 2021. DOI: https://doi.org/10.1007/s12652-024-04916-6.
  6. J. Kitt, A. Frost, J. Mollison, K. L. Tucker, K. Suriano, Y. Kenworthy, A. McCourt, W. Woodward, C. Tan, W. Lapidaire, and R. Mills,“Postpartum blood pressure self-management following hypertensive pregnancy: protocol of the Physician Optimised Post-partum Hypertension Treatment (POP-HT) trial,” BMJ open, vol. 12, no. 2, pp.e051180, 2022. DOI: https://doi.org/10.1136/bmjopen-2021-051180.
  7. S. Tasoujian “Linear Parameter Varying Control of Uncertain Time-Delay Systems with Applications to Automated Blood Pressure Regulation,” (2020).
  8. J. G. Wang, Y. Li, Y. C. Chia, H. M. Cheng, H. V. Minh, S. Siddique, G. P. Sogunuru, J. C. Tay, B. W. Teo, K. Tsoi, and Y. Turana, “Telemedicine in the management of hypertension: evolving technological platforms for blood pressure telemonitoring,” The Journal of Clinical Hypertension, vol. 23, no. 3, pp. 435-439, 2021. DOI: https://doi.org/10.1111/jch.14194.
  9. E. C. Sandset, C. S. Anderson, P. M. Bath, H. Christensen, U. Fischer, D. Gąsecki, A. Lal, L. S. Manning, S. Sacco, T. Steiner,and G. Tsivgoulis,“European Stroke Organisation (ESO) guidelines on blood pressure management in acute ischaemic stroke and intracerebral haemorrhage,” European stroke journal, vol. 6, no. 2, 2021. DOI: https://doi.org/10.1177/23969873211012133.
  10. T. Panula, J. P. Sirkiä, D. Wong, and M. Kaisti“Advances in non-invasive blood pressure measurement techniques,” IEEE Reviews in Biomedical Engineering, vol. 16, pp. 424-438, 2022. DOI: https://doi.org/10.1109/RBME.2022.3141877.
  11. B. Citoni, I. Figliuzzi, V. Presta, M. Volpe, and G. Tocci “Home blood pressure and telemedicine: a modern approach for managing hypertension during and after COVID-19 pandemic,” High Blood Pressure & Cardiovascular Prevention, vol. 29, no. 1, pp. 1-14, 2022. DOI: https://doi.org/10.1007/s40292-021-00492-4.
  12. P. Schoettker, J. Degott, G. Hofmann, M. Proença, G. Bonnier, A. Lemkaddem, M. Lemay, R. Schorer, U. Christen, J. F. Knebel, and A. Wuerzner, “Blood pressure measurements with the OptiBP smartphone app validated against reference auscultatory measurements,” Scientific Reports, vol. 10, no. 1, pp. 17827, 2020. DOI: https://doi.org/10.1038/s41598-020-74955-4.
  13. T. Gazit, M. Gutman, and A. L. Beatty“Assessment of hypertension control among adults participating in a mobile technology blood pressure self-management program,” JAMA Network Open, vol. 4, no. 10, pp. e2127008-e2127008, 2021. DOI: 10.1001/jamanetworkopen.2021.27008.
  14. V. T. Mai, K. A. Alattas, Y. Bouteraa, E. Ghaderpour, and A. Mohammadzadeh,“Personalized Blood Pressure Control by Machine Learning for Remote Patient Monitoring,”IEEE Access, vol. 12, pp.83994-84004, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3413572.
  15. T. Fujiwara, S. Hoshide, H. Kanegae, and K. Kario,“Cardiovascular event risks associated with masked nocturnal hypertension defined by home blood pressure monitoring in the J-HOP nocturnal blood pressure study,” Hypertension, vol. 76, no. 1, pp. 259-266, 2020. DOI: https://doi.org/10.1161/HYPERTENSIONAHA.120.14790.
  16. J. Leitner, P. H. Chiang, and S. Dey,“Personalized blood pressure estimation using photoplethysmography: A transfer learning approach,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 1, pp. 218-228, 2021. DOI: https://doi.org/10.1109/JBHI.2021.3085526.
  17. N. Pachauri, and G. B. K,“Automatic drug infusion control based on metaheuristic H2 optimal theory for regulating the mean arterial blood pressure,”Asia‐Pacific Journal of Chemical Engineering, vol. 16, no. 4, pp. e2654, 2021. DOI: https://doi.org/10.1002/apj.2654.
  18. B. Aubouin-Pairault, M. Fiacchini, and T. Dang,“Automated multi-drugs administration during total intravenous anesthesia using multi-model predictive control,”arXiv preprint arXiv:2309.08229, 2023. DOI: https://doi.org/10.48550/arXiv.2309.08229
  19. K. Saravanakumar, and J. Samson Isaac. “IMO-PSO FO-PID controller based insulin infusion system for type 1 diabetes patients during post-operation condition,” Measurement: Sensors, vol. 33, pp. 101172, 2024. DOI: https://doi.org/10.1016/j.measen.2024.101172.
  20. M. R. Ahmadpour, H. Ghadiri, and S. R. Hajian, “Model predictive control optimisation using the metaheuristic optimisation for blood pressure control,” IET Systems Biology, vol. 15, no. 2, pp. 41-52, 2021. DOI: https://doi.org/10.1049/syb2.12012.
  21. V. Dubey, H. Goud, P. C. Sharma, and S. Anjana, “Designing of intelligent PID controller for cardiac pacemaker using artificial bee colony algorithm,” Systems Science & Control Engineering, vol. 12, no. 1, pp. 2347891, 2024. DOI: https://doi.org/10.1080/21642583.2024.2347891.
  22. V. Dubey, H. Goud, P. C. Sharma, and S. Anjana, “Designing of intelligent PID controller for cardiac pacemaker using artificial bee colony algorithm,” Systems Science & Control Engineering, vol.12, no. 1, pp. 2347891, 2024. DOI: https://doi.org/10.1080/21642583.2024.2347891.
  23. M. R. Ahmadpour, H. Ghadiri, and S. R. Hajian, “Model predictive control optimisation using the metaheuristic optimisation for blood pressure control,” IET Systems Biology, vol. 15, no. 2, pp. 41-52, 2021. DOI: https://doi.org/10.1049/syb2.12012.
  24. R. Haamed, “Controlling of Mean Arterial Pressure by Modified PI-ID Controller Based on Two Optimization Algorithms,” 2020. DOI: 10.5815/ijmecs.2020.04.04.
  25. R. Haamed, and E. Hameed, “Controlling the mean arterial pressure by modified model reference adaptive controller based on two optimization algorithms,” Applied Computer Science, vol. 16, no. 2, 2020. DOI: http://dx.doi.org/10.23743/acs-2020-12.

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