| Title |
Blood Pressure Estimation Using Graph Convolutional Neural Networks with Dynamic Adjacency Matrix |
| Authors |
(Youngshin Kang) ; (Cheolsoo Park) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.4.512 |
| Keywords |
Ballistocardiogram (BCG); Blood pressure estimation; Electrocardiogram (ECG); Euclidean distance; Graph neural network (GNN); Partial direct coherence (PDC); Photoplethysmography (PPG) |
| 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. |