Early detection of high blood pressure from natural speech sounds with graph diffusion network.
Journal:
Computers in biology and medicine
PMID:
39718053
Abstract
This study presents an innovative approach to cuffless blood pressure prediction by integrating speech and demographic features. With a focus on non-invasive monitoring, especially in remote regions, our model harnesses speech signals and demographic data to accurately estimate blood pressure. We found a strong correlation between our predictive model and early-stage high blood pressure, highlighting its potential for early detection. Central to our investigation is the Graph Diffusion Network (GDN) model, achieving exceptional performance with an R score of 0.96 and a Pearson correlation coefficient (PCC) of 0.98. In early-stage hypertension detection, the GDN model achieved an F1-Score of 0.8735 ± 0.10 and accuracy of 0.8896 ± 0.11. Additionally, without considering demographic features, the model still performed well, with an R of 0.740 and PCC of 0.764 when used alone. These results emphasize the value of combining speech and demographic features, offering a promising, non-invasive solution for blood pressure monitoring.