The New HEMS Modelling of Human Heart

Öz

The new version of the hydro-electro-mechanical system (HEMS) is modeled via 14 serially connected electrical equivalent circuits resulting in an integrated equivalent circuit. The new model accepts a group of variables and even examines the interaction between them. This paper introduces an improved integrated new model of the heart by replacing the monolithic equivalent structures with segmental comprehensive equivalents. Windkessel Model (WM) is a model of the relationships between aorta, aortic valve and left ventricle. Based on WM, the integrated new model was developed and simulated. The model’s main focus is to define the dynamic properties of the system by a set of ordinary differential equations, and solving them using Ode23, a method for the solution of a closed-loop system. Using Matlab based Ode23 method; time-dependency of pressure, volume and flow were obtained. In case, short computation time and high accuracy are needed, then ode23 is used. The model may be used to analyze complex processes in the heart and blood vessels. The new HEMS model has potential use for hemodynamic simulation of diseases, cardiovascular disorders, and special congenital heart diseases; such as ASD, VSD and PDA.

Anahtar Kelimeler

• Time-dependent average pressure
• biomedical engineering
• medical control systems
• medical simulation

Kaynakça

1. O. Frank,” Die Grundform des arteriellen Pulses. Erste Abhandlung. Mathematische Analyse.” Z. Biol. 37, 483–526, 1899. DOI: 10.1016/0022-2828(90)91459-k.
2. Rosalia L., Ozturk C., Van Story D., Horvath M.A. and Roche E.T., "Object‐Oriented Lumped‐Parameter Modeling of the Cardiovascular System for Physiological and Pathophysiological Conditions.", Adv. Theory Simul., 4: 2000216, 2021. DOI:10.1002/adts.202000216.
3. Westerhof B.E., Van Gemert M.J.C. and Van den Wijngaard J.P., “Pressure and Flow Relations in the Systemic Arterial Tree Throughout Development From Newborn to Adult.", Front. Pediatr. 8:251, 2020. DOI: 10.3389/fped.2020.00251.
4. Ajmal A., Tananant B., Andres J. R., Vinh N. D. L., Jessica C. R., "Investigation of optical heart rate sensors in wearables and the influence of skin tone and obesity on photoplethysmography (PPG) signal," Proc. SPIE 11638, Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables II, 1163808, 2021. DOI:10.1117/12.2578023.
5. Padmos R.M., Józsa T.I., ElBouri W.K., Konduri P.R., Payne S.J., Hoekstra A.G.,"Coupling one-dimensional arterial blood flow to three-dimensional tissue perfusion models for in silico trials of acute ischaemic stroke.", Interface Focus 11:20190125, 2021. DOI:10.1098/rsfs.2019.0125.
6. Morany, A., Lavon, K., Bluestein, D. et al., "Structural Responses of Integrated Parametric Aortic Valve in an Electro-Mechanical Full Heart Model.", Ann Biomed Eng 49, pp. 441–454, 2021. DOI: 10.1007/s10439-020-02575-0.
7. Roy D., Mazumder O., Sinha A. and Khandelwal S., "Multimodal cardiovascular model for hemodynamic analysis: Simulation study on mitral valve disorders." Plos One, Vol. 16(3):e0247921, 2021. DOI:10.1371/journal.pone.0247921.
8. S. Shimizu, D. Une and T. Kawada, “Lumped parameter model for hemodynamic simulation of congenital heart diseases”, The Journal of Physiological Sciences, Vol. 68, pp. 103–111, 2018. DOI: 10.1007/s12576-017-0585-1.

9. F. Yalcinkaya, E. Kizilkaplan and A. Erbas, “Mathematical modelling of human heart as a hydroelectromechanical system”, 8th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, 2013. DOI:10.1109/ELECO.2013.6713862.
10. Trzaska Z., "Study of mixed-mode oscillations in a nonlinear cardiovascular system." Nonlinear Dyn 100, 2635–2656, 2020. DOI: 10.1007/s11071-020-05612-8.
11. Jamie R. M., and Jiun W.,"Expanding application of the Wiggers diagram to teach cardiovascular physiology.", The American Physiological Society, Vol. 38:2, pp. 170-175, 2014. DOI: 10.1152/advan.00123.2013.
12. Otto A. Smiseth, John M Aalen, Helge Skulstad, “Heart failure and systolic function: time to leave diagnostics based on ejection fraction?”, European Heart Journal, Vol. 42:7, 14 February 2021, pp. 786–788, 2021. DOI: 10.1093/eurheartj/ehaa979.
13. June-Chiew H., Denis L., Andrew T. and Kenneth T., "Re-visiting the Frank-Starling nexus", Progress in Biophysics and Molecular Biology, Vol. 159, pp. 10-21, 2021. DOI:10.1016/j.pbiomolbio.2020.04.003.
14. Cattermole G.N., Leung P.Y., Ho G.Y., et al., “The normal ranges of cardiovascular parameters measured using the ultrasonic cardiac output monitor.” Physiol Rep. 2017; 5(6):e13195. DOI:10.14814/phy2.13195.
15. Comunale G., Peruzzo P., Castaldi B. et al., “Understanding and recognition of the right ventricular function and dysfunction via a numerical study”, Sci Rep 11, 3709, 2021. DOI:/10.1038/s41598-021-82567-9.
16. Bahnasawy S., Al‐Sallami H., Duffull S., "A minimal model to describe short‐term haemodynamic changes of the cardiovascular system." Br J Clin Pharmacol 2021; 87: 1411– 1421. DOI:/10.1111/bcp.14541.
17. Roy D., Mazumder O., Sinha A. and Khandelwal S., "Multimodal cardiovascular model for hemodynamic analysis: Simulation study on mitral valve disorders." PLoS ONE 2021; 16(3):e0247921. DOI:/10.1371/journal.pone.0247921.