Benchmarking and Enhancing PPG-Based Cuffless Blood Pressure Estimation Methods
Department of Engineering Technology, University of Houston, Texas, USA（工程科技系，休斯顿大学，德克萨斯州，美国）；Department of Electrical and Computer Engineering, University of Houston, Texas, USA（电气与计算机工程系，休斯顿大学，德克萨斯州，美国）；Department of Information Science Technology, University of Houston, Texas, USA（信息科学科技系，休斯顿大学，德克萨斯州，美国）；Department of Cardiovascular Surgery, Houston Methodist Hospital, Texas, USA（心血管外科系，休斯顿方法医院，德克萨斯州，美国）；Department of Biomedical Engineering, University of Houston, Texas, USA（生物医学工程系，休斯顿大学，德克萨斯州，美国）
Neville Mathew, Yidan Shen, Renjie Hu, Maham Rahimi, George Zouridakis
Cuffless blood pressure screening based on easily acquired photoplethysmography (PPG) signals offers a practical pathway toward scalable cardiovascular health assessment. Despite rapid progress, existing PPG-based blood pressure estimation models have not consistently achieved the established clinical numerical limits such as AAMI/ISO 81060-2, and prior evaluations often lack the rigorous experimental controls necessary for valid clinical assessment. Moreover, the publicly available datasets commonly used are heterogeneous and lack physiologically controlled conditions for fair benchmarking. To enable fair benchmarking under physiologically controlled conditions, we created a standardized benchmarking subset NBPDB comprising 101,453 high-quality PPG segments from 1,103 healthy adults, derived from MIMIC-III and VitalDB. Using this dataset, we systematically benchmarked several state-of-the-art PPG-based models. The results showed that none of the evaluated models met the AAMI/ISO 81060-2 accuracy requirements (mean error $<$ 5 mmHg and standard deviation $<$ 8 mmHg). To improve model accuracy, we modified these models and added patient demographic data such as age, sex, and body mass index as additional inputs. Our modifications consistently improved performance across all models. In particular, the MInception model reduced error by 23\% after adding the demographic data and yielded mean absolute errors of 4.75 mmHg (SBP) and 2.90 mmHg (DBP), achieves accuracy comparable to the numerical limits defined by AAMI/ISO accuracy standards. Our results show that existing PPG-based BP estimation models lack clinical practicality under standardized conditions, while incorporating demographic information markedly improves their accuracy and physiological validity.
Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG
AI4Health Department, Oldenburg University（AI4Health部门，旧恩斯特堡大学）；Department of Data Science and AI, National Physical Laboratory（数据科学与人工智能系，国家物理实验室）；School of Mathematics and Physics, University of Surrey（数学与物理学院，萨里大学）；Department of Public Health and Primary Care, University of Cambridge（公共卫生与初级保健系，剑桥大学）
Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios
Faculty of Frontier Sciences, Harbin Institute of Technology, Shenzhen（前沿科学学院，哈尔滨工业大学深圳校区）；University of Melbourne（墨尔本大学）；University of New South Wales（新南威尔士大学）
Generalizable Blood Pressure Estimation from Multi-Wavelength PPG Using Curriculum-Adversarial Learning
Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of California, Davis（加州大学戴维斯分校电气与计算机工程系）
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach
Infant Research Centre（婴儿研究中心）；School of Computing（计算机学院）；Dublin City University（都柏林城市大学）；Department of Electrical Engineering（电气工程系）；KU Leuven（鲁汶大学）；Cork University Maternity Hospital（科克大学妇产医院）；Insight SFI Research Centre（Insight SFI研究中心）
Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches
Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西特齐大学）；Laboratoire national de métrologie et d’essais（国家计量与测试实验室）；Biomedical Engineering Institute, Kaunas University of Technology（克莱萨斯理工大学生物医学工程研究所）；Physikalisch-Technische Bundesanstalt（德国物理技术部）；National Physical Laboratory（国家物理实验室）；King’s College London（伦敦国王学院）；University of Surrey（萨里大学）；Department of Public Health and Primary Care, University of Cambridge（剑桥大学公共卫生与初级保健系）
Cuffless, calibration-free hemodynamic monitoring with physics-informed machine learning models
University of Utah（犹他大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Utah Health（犹他大学健康中心）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Veterans Affairs Salt Lake City Health Care System（退伍军人事务部盐湖城医疗系统）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；University of Pittsburgh（匹兹堡大学）
TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure Estimation via Solo PPG Signal Capturing
Faculty of Computer Science（计算机科学学院）；Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程学院）；University of New Brunswick（新不伦瑞克大学）；Concordia University（康考迪亚大学）
Change Point--Aware Evaluation and Re-Calibration of PPG-Based Blood Pressure Estimation
VUNO Inc.（VUNO公司）；University of California, Irvine（加州大学欧文分校）；Research Institute for Future Medicine, Samsung Medical Center（三星未来医学研究所）；Inha University Hospital（仁荷大学医院）
End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables
Interuniversity Department of Regional and Urban Studies and Planning at Politecnico di Torino（理工学院区域与城市研究与规划联合大学部门）；Department of Control and Computer Engineering at Politecnico di Torino（理工学院控制与计算机工程系）；Department of Information Technology and Electrical Engineering at ETH Zurich（苏黎世联邦理工学院信息技术与电气工程系）；Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" at University of Bologna（博洛尼亚大学电子、电子与信息工程系"Guglielmo Marconi"）
Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks
University of California Irvine（加利福尼亚大学尔湾分校）；Department of Electrical Engineering and Computer Science, University of California Irvine（加利福尼亚大学尔湾分校电气工程与计算机科学系）；Department of Computer Science, University of California, Irvine（加利福尼亚大学尔湾分校计算机科学系）；Institute for Future Health, University of California, Irvine（加利福尼亚大学尔湾分校未来健康研究所）
Rapid Adaptation of SpO2 Estimation to Wearable Devices via Transfer Learning on Low-Sampling-Rate PPG
Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of California, Davis（加州大学戴维斯分校电气与计算机工程系）
Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models
Verily Life Sciences, AI Team（Verily生命科学公司，人工智能团队）；Technion - Israel Institute of Technology, Computer Science Department（技术学院-以色列理工学院，计算机科学系）；Verily Life Sciences（Verily生命科学公司）
Non-invasive Waveform Analysis for Emergency Triage via Simulated Hemorrhage: An Experimental Study using Novel Dynamic Lower Body Negative Pressure Model
Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）
Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
AI4Health Department, University of Oldenburg（奥尔登堡大学AI4Health部门）；Department of Data Science and AI, National Physical Laboratory（国家物理实验室数据科学与人工智能部门）；School of Mathematics and Physics, University of Surrey（萨里大学数学与物理学院）
