A Machine Learning Approach to Predict Arterial Blood Pressure from Photoplethysmography Signal

Blood pressure (BP) monitoring is a basic procedure for the physiological measurement of the cardiovascular system, especially because high BP, although preventable, is a major risk for stroke, heart failure, and other serious conditions. Photoplethysmography (PPG) is a promising technology developed to allow non-invasive, regular, or even continuous measurement of blood volume variation. Recently, some works have tried to use PPG signals to estimate BP. In this work, we propose a regression model based on the Category Boosting algorithm (Cat-Boost) that uses 133 morphological and temporal features from the PPG signal to estimate the corresponding diastolic and systolic BP. We processed and selected a total of 50,182 windows of 1,000 samples (sampling rate of 125Hz during 8 seconds) of PPG and BP signals from the MIMIC-II dataset, distributed into training and test sets. Three different data cross-validation schemes were adopted. The model prediction metrics were evaluated by Mean Error and standard deviation (ME[STD]), and Pearson's Correlation Coefficient (R-value). For one of the validation schemes, we obtained, for the diastolic BP, 0.02[3.77] mmHg with an R-value of 0.93; and for systolic BP: 0.05[7.84] mmHg with an R-value of 0.93. Our results meet the AAMI standard and are comparable to the state of the art. However, we show that these results rely on a specific validation scheme.