Study of continuous blood pressure estimation based on pulse transit time, heart rate and photoplethysmography-derived hemodynamic covariates.

It is widely recognized that pulse transit time (PTT) can track blood pressure (BP) over short periods of time, and hemodynamic covariates such as heart rate, stiffness index may also contribute to BP monitoring. In this paper, we derived a proportional relationship between BP and PPT and proposed an improved method adopting hemodynamic covariates in addition to PTT for continuous BP estimation. We divided 28 subjects from the Multi-parameter Intelligent Monitoring for Intensive Care database into two groups (with/without cardiovascular diseases) and utilized a machine learning strategy based on regularized linear regression (RLR) to construct BP models with different covariates for corresponding groups. RLR was performed for individuals as the initial calibration, while recursive least square algorithm was employed for the re-calibration. The results showed that errors of BP estimation by our method stayed within the Association of Advancement of Medical Instrumentation limits (- 0.98 ± 6.00 mmHg @ SBP, 0.02 ± 4.98 mmHg @ DBP) when the calibration interval extended to 1200-beat cardiac cycles. In comparison with other two representative studies, Chen's method kept accurate (0.32 ± 6.74 mmHg @ SBP, 0.94 ± 5.37 mmHg @ DBP) using a 400-beat calibration interval, while Poon's failed (- 1.97 ± 10.59 mmHg @ SBP, 0.70 ± 4.10 mmHg @ DBP) when using a 200-beat calibration interval. With additional hemodynamic covariates utilized, our method improved the accuracy of PTT-based BP estimation, decreased the calibration frequency and had the potential for better continuous BP estimation.