Deep learning advances in photoplethysmography prediction of hypertension
GAO Haoran
JIANG Mei
KONG Lin
ZHANG Kuixing
LI Xueying
Abstract：Hypertension is one of the most common cardiovascular diseases,continuous blood pressure monitoring is essential for early detection and management of hypertension.Photoplethysmography(PPG)as a noninvasive physiological sensing technique,has been widely used in medical monitoring.Recent studies show that integrating deep learning with PPG signals enables effective blood pressure estimation and improves real-time monitoring accuracy.This review systematically summarizes the latest research progress of deep learning models such as Transformers,graph neural network(GNN)and transfer learning,and compares the application charac-teristics and performance of each model in PPG modeling.Finally,the advantages and disadvantages of various models are summarized,and future research directions are proposed to provide references and inspirations for non-invasive prediction and intelligent monitoring of hypertension.
Keywords：HypertensionEarly detectionNoninvasivePhotoplethysmographyDeep learning
Publication Date：2026-06-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：6( 260-265 )