As non-invasive continuous blood pressure monitoring (NCBPM) has gained wide attraction in the recent decades, many pulse arrival time (PAT) or pulse transit time (PTT) based blood pressure (BP) estimation studies have been conducted. However, most of the studies have used small homogeneous subject pools to generate models of BP based on particular interventions for induced hemodynamic change. In this study, a large open biosignal database from a diverse group of 2309 surgical patients was analyzed to assess the efficacy of PAT, PTT, and confounding factors on the estimation of BP. After pre-processing the dataset, a total of 6,777,308 data pairs of BP and temporal features between electrocardiogram (ECG) and photoplethysmogram (PPG) were extracted and analyzed. Correlation analysis revealed that PAT or PTT extracted from the intersecting-tangent (IT) point of PPG showed the highest mean correlation to BP. The mean correlation between PAT and systolic blood pressure (SBP) was −0.37 and the mean correlation between PAT and diastolic blood pressure (DBP) was −0.30, outperforming the correlation between BP and PTT at −0.12 for SBP and −0.11 for DBP. A linear model of BP with a simple calibration method using PAT as a predictor was developed which satisfied international standards for automatic oscillometric BP monitors in the case of DBP, however, SBP could not be predicted to a satisfactory level due to higher errors. Furthermore, multivariate regression analyses showed that many confounding factors considered in previous studies had inconsistent effects on the degree of correlation between PAT and BP.
Noninvasive and continuous blood pressure monitoring with better accuracy by combining pulse arrival time and peak delay
