Blood Pressure Estimation Using Graph Convolutional Neural Networks with Dynamic Adjacency Matrix

Abstract

The estimation of blood pressure using pulse transit time is critical for the continuous monitoring of blood pressure during our daily lives. This study addresses the challenge by introducing a novel, non-invasive approach for blood pressure estimation that does not rely on pulse transit time (PTT). Instead, we propose an innovative graph-based neural network architecture that leverages the interconnectedness of multiple physiological signals, specifically ballistocardiogram, photoplethysmogram, and electrocardiogram. Specifically, the adjacency matrix for the graph neural networks is constructed with one-way and two-way directional relationships among the physiological signals, Euclidean distance, and causality. The findings suggest that our graph-based neural network model holds significant potential for enhancing continuous, non-invasive blood pressure monitoring, thereby contributing to better cardiovascular health management in everyday life.