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PhysCL: Knowledge-Aware Contrastive Learning of Physiological Signal Models for Cuff-Less Blood Pressure Estimation

文献类型：期刊论文

作者Liu, Renju1,2; Shen, Jianfei2; Gu, Yang2; Chen, Yiqiang2; Zhang, Jiling3; Wu, Qingyu2; Xu, Chenyang4; Fan, Feiyi2
刊名IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
出版日期2025-07-01
卷号29期号:7页码:4728-4740
关键词Blood pressure Physiology Contrastive learning Data augmentation Semantics Feature extraction Estimation Time series analysis Training Data models Blood pressure estimation physiological signal processing representation learning self-supervised learning
ISSN号2168-2194
DOI10.1109/JBHI.2025.3554495
英文摘要Training deep learning models for photoplethysmography(PPG)-based cuff-less blood pressure estimation often requires a substantial amount of labeled data collected through sophisticated medical instruments, posing significant challenges in practical applications. To address this issue, we propose Physiological Knowledge-Aware Contrastive Learning (PhysCL), a novel approach designed to reduce the dependence on labeled PPG data while improving blood pressure estimation accuracy. Specifically, PhysCL tackles the semantic consistency problem in contrastive learning by introducing a knowledge-aware augmentation bank, which generates positive physiological signal pairs using knowledge-based constraints during the contrastive pair generation. Additionally, we propose a contrastive feature reconstruction method to enhance feature diversity and prevent model collapse through feature re-sampling and re-weighting. We evaluate PhysCL on data from 106 subjects across the MIMIC III, MIMIC IV, and UQVS datasets under cross-dataset validation settings, comparing it against state-of-the-art contrastive learning methods and blood pressure estimation models. PhysCL achieves an average mean absolute error of 9.5/5.9 mmHg (systolic/diastolic) across the three datasets, using only 2% labeled data combined with 98% unlabeled data for pre-training and 5 samples for personalization, which represents a 6.2% /4.3% improvement, respectively, over the current best supervised methods. The ablation study provides further convincing evidence that the unlabeled data can be utilized to improve the existing cuff-less blood pressure estimation models and shed light on unsupervised contrastive learning for physiological signals.
资助项目National Natural Science Foundation of China[62101530] ; Beijing Municipal Science & Technology Commission[Z221100002722009] ; Youth Innovation Promotion Association CAS[2021101] ; AI Dream Project[SZSM202401] ; AI Dream Project[SZSM202403]
WOS研究方向Computer Science ; Mathematical & Computational Biology ; Medical Informatics
语种英语
WOS记录号WOS:001523482700009
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/42020]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Fan, Feiyi
作者单位1.Beijing Normal Univ Hong Kong Baptist Univ United, Dept Comp Sci, Zhuhai 519087, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.Zhejiang Univ, Sch Software Technol, Hangzhou 310058, Peoples R China
4.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Renju,Shen, Jianfei,Gu, Yang,et al. PhysCL: Knowledge-Aware Contrastive Learning of Physiological Signal Models for Cuff-Less Blood Pressure Estimation[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2025,29(7):4728-4740.
APA Liu, Renju.,Shen, Jianfei.,Gu, Yang.,Chen, Yiqiang.,Zhang, Jiling.,...&Fan, Feiyi.(2025).PhysCL: Knowledge-Aware Contrastive Learning of Physiological Signal Models for Cuff-Less Blood Pressure Estimation.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,29(7),4728-4740.
MLA Liu, Renju,et al."PhysCL: Knowledge-Aware Contrastive Learning of Physiological Signal Models for Cuff-Less Blood Pressure Estimation".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 29.7(2025):4728-4740.

入库方式： OAI收割

来源：计算技术研究所

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