No abstract

Objective: Single-site photoplethysmography (PPG)-based blood pressure (BP) estimation has raised a lot of interest due to its compactness and low cost. However, this method relies on PPG morphological features, which are sensitive to noise and measurement conditions. The underlying physiological mechanism was also unclear at this moment. In this study, we propose to add timescale patterns of PPG to improve the BP estimation performance and clarify the underlying mechanism. Methods: In-silico simulation with a four-element Windkessel model showed that peripheral resistance and vascular compliance variation during a cardiac cycle correlated with PPG’s long- and short-term self-similarity, which significantly correlated with BP. A publicly available dataset was used to validate the simulation predictions using mutual information analysis and regression performance assessment. Results: The hemodynamic property of the cardiovascular system determines how fast PPG responds to a stimulus. High vascular compliance or peripheral resistance leads to prolonged and overlapped responses, which could be described by timescale patterns. Adding these patterns significantly increased the PPG-BP mutual information and improved the BP estimation performance. Compared to algorithms with morphological and biometric features, the mean absolute error (MAE) of calibrated systolic BP (SBP) reduced from 5.37mmHg to 4.51mmHg, while the MAE of calibration-free diastolic BP (DBP) reduced from 3.46mmHg to 2.81mmHg. The median intra-subject correlation between SBP/DBP estimation and ground truth increased from 0.63/0.34 to 0.80/0.68, which means the intrinsic BP fluctuation was better captured. Conclusion: Timescale patterns were vital to single-site PPG-based BP estimation. Understanding its physiological implication may help us design algorithms with clear interpretability and simplified structures.

Objective: Single-site photoplethysmography (PPG)-based blood pressure (BP) estimation has raised a lot of interest due to its compactness and low cost. However, this method relies on PPG morphological features, which are sensitive to noise and measurement conditions. The underlying physiological mechanism was also unclear at this moment. In this study, we propose to add timescale patterns of PPG to improve the BP estimation performance and clarify the underlying mechanism. Methods: In-silico simulation with a four-element Windkessel model showed that peripheral resistance and vascular compliance variation during a cardiac cycle correlated with PPG’s long- and short-term self-similarity, which significantly correlated with BP. A publicly available dataset was used to validate the simulation predictions using mutual information analysis and regression performance assessment. Results: The hemodynamic property of the cardiovascular system determines how fast PPG responds to a stimulus. High vascular compliance or peripheral resistance leads to prolonged and overlapped responses, which could be described by timescale patterns. Adding these patterns significantly increased the PPG-BP mutual information and improved the BP estimation performance. Compared to algorithms with morphological and biometric features, the mean absolute error (MAE) of calibrated systolic BP (SBP) reduced from 5.37mmHg to 4.51mmHg, while the MAE of calibration-free diastolic BP (DBP) reduced from 3.46mmHg to 2.81mmHg. The median intra-subject correlation between SBP/DBP estimation and ground truth increased from 0.63/0.34 to 0.80/0.68, which means the intrinsic BP fluctuation was better captured. Conclusion: Timescale patterns were vital to single-site PPG-based BP estimation. Understanding its physiological implication may help us design algorithms with clear interpretability and simplified structures.