UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions
Abstract 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-...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02963-3 |
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| author | Yali Zheng Hongda Huang Jiasheng Gao Jingyuan Hong Shenghao Wu Yuanting Zhang Qing Liu |
| author_facet | Yali Zheng Hongda Huang Jiasheng Gao Jingyuan Hong Shenghao Wu Yuanting Zhang Qing Liu |
| author_sort | Yali Zheng |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
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| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-2e2d5a59fd0a425aa063371a18a7194f2025-08-20T02:33:31ZengNature PortfolioScientific Reports2045-23222025-05-0115111210.1038/s41598-025-02963-3UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditionsYali Zheng0Hongda Huang1Jiasheng Gao2Jingyuan Hong3Shenghao Wu4Yuanting Zhang5Qing Liu6 Department of Biomedical Engineering, College of Health Science and Environmental Engineering, ShenzhenTechnology University Department of Biomedical Engineering, College of Health Science and Environmental Engineering, ShenzhenTechnology University Department of Biomedical Engineering, College of Health Science and Environmental Engineering, ShenzhenTechnology University School of Imaging Sciences and Biomedical Engineering, King’s College London Department of Biomedical Engineering, College of Health Science and Environmental Engineering, ShenzhenTechnology UniversityChinese University of Hong KongDepartment of Communication and Networking, Xi’an Jiaotong-Liverpool UniversityAbstract 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.https://doi.org/10.1038/s41598-025-02963-3Cuffless blood pressure estimationCross-scenarioModel generalizabilityCalibration-freeDistribution shiftDistribution imbalance |
| spellingShingle | Yali Zheng Hongda Huang Jiasheng Gao Jingyuan Hong Shenghao Wu Yuanting Zhang Qing Liu UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions Scientific Reports Cuffless blood pressure estimation Cross-scenario Model generalizability Calibration-free Distribution shift Distribution imbalance |
| title | UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions |
| title_full | UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions |
| title_fullStr | UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions |
| title_full_unstemmed | UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions |
| title_short | UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions |
| title_sort | utransbpnet for cuffless and calibration free blood pressure estimation under dynamic conditions |
| topic | Cuffless blood pressure estimation Cross-scenario Model generalizability Calibration-free Distribution shift Distribution imbalance |
| url | https://doi.org/10.1038/s41598-025-02963-3 |
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