Advancing cuffless arterial blood pressure estimation: A patient-specific optimized approach reducing computational requirements

Abstract

Hypertension remains a leading cause of premature mortality globally, emphasizing the critical need for early detection and management. Unfortunately, less than half of hypertensive adults receive proper diagnosis and treatment. To address this gap, continuous blood pressure (ABP) monitoring has emerged as a valuable tool for detecting cardiovascular complications before they escalate. ABP monitoring can be achieved by cuffless ABP estimation techniques embedded on wearables. In this paper, we present an innovative personalized medicine approach for cuffless arterial blood pressure estimation, characterized by its patient-specific focus and computational requirements reduction. An XGBoost patient specific ABP estimator model is optimized for each patient through Bayesian techniques, using their photoplethysmogram (PPG) features. The proposed method achieves a mean absolute error (MAE) of 7.27 mmHg for systolic and 3.33 mmHg for diastolic blood pressure. Additionally, recursive feature elimination techniques are used to streamline the model, making it suitable for resource-limited environments such as wearables platforms. This combination of approaches offers a promising outlook for the application of personalized medicine in blood pressure monitoring, thereby enhancing hypertension management and reducing associated health risks.