Combining impedance cardiography with Windkessel model for blood pressure estimation

Given that blood pressure is a vital indicator of cardiovascular health, the domain of non-invasive continuous blood pressure monitoring has emerged as a hot area of interest in current research. However, existing studies in this field are often constrained by their limited capacity for clinical physiological interpretation and for reflecting cardiovascular and hemodynamic information. This gap hinders their effectiveness in elucidating the influence of cardiovascular system changes on blood pressure. This study aims to address these issues by using impedance cardiogram signal and the Windkessel (WK) model. First, we extracted features representing hemodynamic parameters from impedance cardiogram signal. Then, these features were utilized alongside the XGBoost algorithm to estimate parameters within the WK model. Finally, this model was used to model the subject’s cardiovascular system, thereby precisely simulating and estimating blood pressure changes. This methodology was validated using a public dataset, with results indicating that in resting scenario, the mean absolute error for systolic blood pressure and diastolic blood pressure were 4.72 mmHg and 3.72 mmHg, respectively. Furthermore, our findings identified a positive correlation between the WK model’s resistance parameter and blood pressure, and a negative correlation between its compliance parameter and blood pressure. These insights are instrumental in pioneering new avenues for continuous blood pressure estimation and in deepening our understanding of the physiological mechanisms of blood pressure changes.