Cardiovascular System Modeling Using Windkessel Segmentation Model Based on Photoplethysmography Measurements of...

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Signal Processing

Background:

• Photoplethysmography (PPG) signals contain vital information about cardiovascular health.
• Cardiovascular system modeling using PPG enables analysis and prediction of the system's condition.

Purpose of the Study:

• To develop a cardiovascular system model using a segmented Windkessel model.
• To analyze fingertip and toe PPG signals to determine blood vessel elasticity.
• To focus on PPG parameters like stiffness index, time delay (Δt), and augmentation index.

Main Methods:

• Dividing blood vessels into seven segments for a Windkessel model.
• Calculating Resistance, Inductance, and Capacitance (RLC) values for each segment.
• Equating electronic units with cardiovascular units to derive RLC values.

Main Results:

• Simulated PPG signals using PSpice showed high correlation (>0.9) with measured data.
• A 20% decrease in capacitance and arterial stiffness parameters aligned simulated data with cardiac patient measurements.
• The model effectively differentiated between healthy individuals and cardiac patients.

Conclusions:

• The proposed model facilitates early detection of arterial stiffness.
• It offers a tool for studying cardiovascular dynamics, including blood flow and pressure.
• This approach enhances understanding of PPG signal behavior in relation to cardiovascular health.