Cardiovascular system modeling using windkessel segmentation model based on photoplethysmography measurements of fingers and toes

Ervin Masita Dewi, Sugondo Hadiyoso, Tati Latifah Erawati Rajab Mengko, Hasballah Zakaria, Kastam Astami

DOI: 10.4103/jmss.jmss_101_21

Abstract


Background: Photoplethysmography (PPG) contains information about the health condition of the heart and blood vessels. Cardiovascular system modeling using PPG signal measurements can represent, analyze, and predict the cardiovascular system. Methods: This study aims to make a cardiovascular system model using a Windkessel model by dividing the blood vessels into seven segments. This process involves the PPG signal of the fingertips and toes for further analysis to obtain the condition of the elasticity of the blood vessels as the main parameter. The method is to find the Resistance, Inductance, and Capacitance (RLC) value of each segment of the body through the equivalent equation between the electronic unit and the cardiovascular unit. The modeling made is focused on PPG parameters in the form of stiffness index, the time delay (?t), and augmentation index. Results: The results of the model simulation using PSpice were then compared with the results of measuring the PPG signal to analyze changes in the behavior of the PPG signal taken from ten healthy people with an average age of 46 years, compared to ten cardiac patients with an average age of 48 years. It is found that decreasing 20% of capacitance value and the arterial stiffness parameter will close to cardiac patients' data. Compared with the measurement results, the correlation of the PPG signal in the simulation model is more than 0.9. Conclusions: The proposed model is expected to be used in the early detection of arterial stiffness. It can also be used to study the dynamics of the cardiovascular system, including changes in blood flow velocity and blood pressure.

Keywords


Cardiovascular system, finger and toe photoplethysmography, photoplethysmography, stiffness index, Windkessel segmentation model

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References


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