Parametric Study of Cardiovascular Parameter Influence on PPG-to-CBP Signal Conversion.

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Signal Processing

Background:

• Non-invasive monitoring of central blood pressure (CBP) is crucial for cardiovascular health management.
• Photoplethysmography (PPG) offers a promising, wearable method for continuous CBP estimation.
• Current PPG-based CBP models face challenges in accurately accounting for physiological variations.

Purpose of the Study:

• To investigate the impact of heart rate, stroke volume, and peripheral resistance on the accuracy of predicting CBP from PPG signals.
• To evaluate the performance of system identification models in a controlled cardiovascular simulation environment.
• To identify key factors influencing the reliability of PPG-derived CBP measurements.

Main Methods:

• Utilized a novel cardiovascular simulator and a custom wrist phantom to generate realistic PPG and blood pressure waveforms.
• Systematically varied heart rate, stroke volume, and peripheral resistance to mimic diverse physiological conditions.
• Applied ensemble averaging and system identification techniques to predict CBP from simulated PPG signals.

Main Results:

• Ensemble averaging achieved high accuracy (93.06% for stroke volume, 95.38% for peripheral resistance) in predicting CBP waveforms.
• Performance degraded significantly with heart rate variations, showing only 83.65% accuracy.
• The study highlights the differential impact of cardiovascular parameters on PPG-to-CBP conversion accuracy.

Conclusions:

• Parameter-specific modeling approaches are essential for improving the accuracy of non-invasive CBP monitoring using wearable PPG devices.
• Addressing heart rate variability is critical for robust PPG-based CBP estimation.
• This research provides insights for developing more reliable and clinically relevant wearable cardiovascular monitoring systems.