Scenario Adaptive Cuffless Blood Pressure Estimation by Integrating Cardiovascular Coupling Effects

Adding cuffless blood pressure (BP) measurement function to wearable devices is of great value in the fight against hypertension. The widely used arterial pulse transit time (PTT)-based method for BP monitoring relies primarily on vascular status-determined BP models and typically exhibits degraded performance over time and is sensitive to measurement procedures. Developing alternative methods with improved accuracy and adaptability to various application scenarios is highly desired for cuffless BP measurement. In this work, we proposed a pattern-fusion (PF) method that incorporates cardiovascular coupling effects in the vascular model by combining three calculation modules - cardiac parameter extraction module, cardiac parameter-to-BP mapping module, and BP regulation module. Specifically, the first module combines feedforward, feedback, and propagation modes to model different modulation functions of a cardiovascular system and is responsible for extracting BP-related features from electrocardiography (ECG) and photoplethysmography (PPG) signals; the cardiac parameter-to-BP mapping module is used to map cardiac parameters into mean blood pressure (MBP) by fusing different features; finally, the BP regulation module recovers accurate systolic BP (SBP) and diastolic BP (DBP) from given MBP. With the concerted use of these three modules, the pattern fusion method consistently demonstrates excellent BP prediction accuracy in a variety of measurement scenarios and durations, exhibiting SBP/DBP mean absolute error (MAE) of 3.65/4.56 mmHg for the short-term (<10 mins) continuous measurement dataset, SBP/DBP MAE of 6.84/3.81 mmHg for the medium-term (avg. > 20 hours) continuous measurement dataset, and SBP/DBP MAE of 6.24/3.65 mmHg for the long-term (>1 month) intermittent measurement dataset.