Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals

Area of Science:

• Biomedical Engineering
• Medical Physics

Background:

• Conventional blood pressure measurement relies on cuff-based devices, which can be uncomfortable and burdensome.
• Accurate blood pressure monitoring is crucial for diagnosing and managing vascular diseases.

Purpose of the Study:

• To develop and evaluate a cuffless blood pressure estimation model.
• To utilize Monte Carlo simulation (MCS) and photoplethysmography (PPG) for blood pressure monitoring.

Main Methods:

• A heterogeneous finger model was developed for MCS at 905 nm and 940 nm wavelengths.
• Photon intensities from MCS were recorded and correlated with PPG signals to estimate blood pressure.
• The model was validated using both public and self-made datasets for transmission- and reflection-type MCS.

Main Results:

• The proposed model demonstrated low mean absolute errors for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) across datasets.
• For transmission-type MCS on a public dataset, errors were 3.32 ± 6.03 mmHg (SBP), 2.02 ± 2.64 mmHg (DBP), and 1.76 ± 2.8 mmHg (MAP).
• Estimated SBP and DBP met AAMI standards and achieved Grade A under BHS standards.

Conclusions:

• The cuffless blood pressure estimation model based on MCS and fingertip PPG signals is efficient and accurate.
• The developed model offers a comfortable and less burdensome alternative to traditional cuff-based methods.
• The results indicate significant potential for widespread clinical application in blood pressure monitoring.