Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression

In this work, we present a photoplethy smography-based blood pressure monitoring algorithm (PPG-BPM) that solely requires a photoplethysmography (PPG) signal. The technology is based on pulse wave analysis (PWA) of PPG signals retrieved from different body locations to continuously estimate the systolic blood pressure (SBP) and the diastolic blood pressure (DBP). The proposed algorithm extracts morphological features from the PPG signal and maps them to SBP and DBP values using a multiple linear regression (MLR) model. The performance of the algorithm is evaluated on the publicly available Multiparameter Intelligent Monitoring in Intensive Care (MIMIC I) database. We utilize 28 data-sets (records) that contain both PPG and brachial arterial blood pressure (ABP) signals. The collected PPG and ABP signals are synchronized and divided into intervals of 30 seconds, called epochs. In total, we utilize 47153 clean 30-second epochs for the performance analysis. Out of the 28 data-sets, we use only 2 data-sets with a total of 2677 clean 30-second epochs to build the MLR model of the algorithm. For the SBP, a mean absolute error (MAE) of 6.10 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.90, p = .001. For the DBP, and an MAE of 4.65 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.85, p < .001. We also use a binary classifier for the BP values with the positives indicating SBP = 130 mmHg and/or DBP = 80 mmHg and the negatives indicating otherwise. The classifier results generated by the PPG-based SBP and DBP estimates achieve a sensitivity and a specificity of 79.11% and 92.37%, respectively.

Automated summary

In this study, a photoplethysmography-based blood pressure monitoring algorithm was proposed, which extracts morphological features from PPG signals and maps them to SBP and DBP values using an MLR model. The algorithm was evaluated on the MIMIC I database, achieving high accuracy in estimating both SBP and DBP.