Journal Article

UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions

Hong, Jingyuan, Zhang, Yuanting, Zheng, Yali, Liu, Qing, Wu, Shenghao, Gao, Jiasheng, Huang, Hongda

2025

Overview

Accurate cuffless blood pressure (BP) estimation remains challenging, particularly under dynamic conditions with significant intra-individual BP variations. This study introduces UTransBPNet , a novel, calibration-free model for cuffless BP estimation. It integrates a squeeze-and-excitation-enhanced Unet architecture for short-range feature extraction with a transformer and cross attention module to capture long-range dependencies from high-resolution, multi-channel physiological signals, further refined through an optimized fine-tuning scheme. Comprehensive validations were conducted across multiple dynamic datasets—Dataset_Drink, Dataset_Exercise, and Dataset_MIMIC—in both scenario-specific and cross-scenario settings. Results demonstrate that UTransBPNet outperformed existing models in tracking BP variations under dynamic conditions, achieving individual Pearson’s correlation coefficients of 0.61 ± 0.17 and 0.62 ± 0.13 for systolic BP (SBP) and diastolic BP (DBP) in Dataset_Drink, 0.82 ± 0.11 and 0.72 ± 0.18 in Dataset_Exercise, and low mean absolute differences (MADs) of 4.38 and 2.25 mmHg in Dataset_MIMIC. The analysis also highlights the impact of dataset characteristics on model performance, such as distribution shift, distribution imbalance and individual BP variability, highlighting the need for well-curated data to ensure generalizability. This study advances the development of robust, cuffless BP estimation models for real-world applications.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/166/985 / 639/166/987 / 639/705/258 / Algorithms / Blood pressure / Blood Pressure - physiology / Blood Pressure Determination - methods / Calibration / Calibration-free / Correlation coefficient / Cross-scenario / Cuffless blood pressure estimation / Datasets / Distribution imbalance / Distribution shift / Humanities and Social Sciences / Humans / Model generalizability / multidisciplinary / Science / Science (multidisciplinary)