IEEE Journal of Biomedical and Health Informatics最新文献

Dual Transformer Network for Predicting Joint Angles and Torques From Multi-Channel EMG Signals in the Lower Limbs.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-27 DOI: 10.1109/JBHI.2025.3555255

Zhuo Wang, Chunjie Chen, Hui Chen, Yizhe Zhou, Xiangyang Wang, Xinyu Wu

{"title":"Dual Transformer Network for Predicting Joint Angles and Torques From Multi-Channel EMG Signals in the Lower Limbs.","authors":"Zhuo Wang, Chunjie Chen, Hui Chen, Yizhe Zhou, Xiangyang Wang, Xinyu Wu","doi":"10.1109/JBHI.2025.3555255","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3555255","url":null,"abstract":"Accurate estimation of lower limb joint kinematics and kinetics using wearable sensors enables biomechanical analysis beyond laboratory settings and facilitates real-time adaptation of exoskeleton assistance profiles. This study introduces a Dual Transformer Network (DTN) designed to concurrently estimate multiple joint angles and moments from multi-channel surface electromyography (sEMG) signals in the lower limbs. The performance evaluation of the predicted joint angles for the hip, knee, and ankle showed average root mean square error (RMSE) values of 1.1827, 1.4312, and 0.8113, Pearson correlation coefficients () of 0.9992, 0.9993, and 0.9991, and coefficients of determination () of 0.9847, 0.9858, and 0.9838, respectively. For the predicted joint moments, the corresponding values were RMSE of 0.0458, 0.0341, and 0.0522 Nm/kg, of 0.9978, 0.9972, and 0.9990, and of 0.9825, 0.9801, and 0.9902. Angular velocities, derived by differentiating the estimated joint angles, achieved an RMSE below 0.6530 rd/s, exceeding 0.9534, and above 0.9552. Additionally, joint power, computed as the dot product of predicted joint moments and angular velocities, resulted in RMSE below 0.3823W/kg, above 0.9771, and above 0.8925. These results demonstrate the effectiveness of the proposed network in continuously estimating lower limb kinematics and kinetics, contributing to advancements in assist-as-needed exoskeleton control strategies.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-IID Medical Image Segmentation Based on Cascaded Diffusion Model for Diverse Multi- Center Scenarios.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-27 DOI: 10.1109/JBHI.2025.3549029

Hanwen Zhang, Mingzhi Chen, Yuxi Liu, Guibo Luo, Yuesheng Zhu

{"title":"Non-IID Medical Image Segmentation Based on Cascaded Diffusion Model for Diverse Multi- Center Scenarios.","authors":"Hanwen Zhang, Mingzhi Chen, Yuxi Liu, Guibo Luo, Yuesheng Zhu","doi":"10.1109/JBHI.2025.3549029","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3549029","url":null,"abstract":"Learning from multi-center medical datasets to obtain a high-performance global model is challenging due to the privacy protection and data heterogeneity in healthcare systems. Current federated learning approaches are not efficient enough to learn Non-Independent and Identically Distributed (Non-IID) data and require high communication costs. In this work, a practical privacy computing framework is proposed to train a Non-IID medical image segmentation model under various multi-center setting in low communication cost. Specifically, an efficient cascaded diffusion model is trained to generate image-mask pairs that have similar distribution to the training data of clients, providing rich labeled data on client side to mitigate heterogeneity. Also, a label construction module is developed to improve the quality of generated image-mask pairs. Moreover, a set of aggregation methods is proposed to achieve global model from data generated from Cascaded Diffusion model for diverse scenarios: CD-Syn, CD-Ens and its extension CD-KD. CD-Syn is a one-shot method that trains segmentation model solely on public generated datasets while CD-Ens and CD-KD maximize the utilization of local original data by an extra communication round of ensemble or knowledge distillation. In this way, the setting of our proposed framework is highly practical, providing multiple aggregation methods which can flexibly adapt to varying demands for efficiency, privacy, and accuracy. We systematically evaluated the effectiveness of our proposed framework on five Non-IID medical datasets and observe 5.38% improvement in Dice score compared with baseline method (FednnU-Net) on average.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Camera-Based Dual-Wavelength Defocused Speckle Imaging for Multi-Point Seismocardiographic Motion Measurement.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-27 DOI: 10.1109/JBHI.2025.3555218

Lin Liu, Qiang Wei, Haozhe Li, Caifeng Shan, Wenjin Wang

{"title":"Camera-Based Dual-Wavelength Defocused Speckle Imaging for Multi-Point Seismocardiographic Motion Measurement.","authors":"Lin Liu, Qiang Wei, Haozhe Li, Caifeng Shan, Wenjin Wang","doi":"10.1109/JBHI.2025.3555218","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3555218","url":null,"abstract":"Continuous monitoring of cardiac activity is crucial for detecting anomalies such as heart failure and coronary artery disease, and it can alleviate the burden of cardiovascular disease on healthcare systems. This study introduces a novel concept for contactless monitoring of multi-point cardiac motion using dual- wavelength defocused speckle imaging (DW-DSI). A prototype system was developed to measure multi-point seismocardiography (MP-SCG) signals from the atrial and ventricular regions. In addition, blood pressure (BP) monitoring was demonstrated as a proof of concept using the time delay between atrial and ventricular motion signals. An experiment involving 19 subjects with ice water stimulation protocol demonstrated that the performance of BP estimation using time delay features of MP-SCG is comparable to BP estimated from ECG-PPG derived pulse arrival time. The results showed that the best performance was achieved using the correlation features extracted from MP-SCG, such as time delay information and heart rate, in combination with an artificial neural network model. The mean absolute error for systolic/diastolic/mean BP are 6.954 mmHg, 5.368 mmHg and 5.415 mmHg, with Pearson correlation coefficient of 0.639, 0.559 and 0.517. This demonstrates the potential of the camera-based DW-DSI system for measuring MP-SCG and the feasibility towards continuous BP monitoring.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepTWA-TM: Deep Learning T-Wave Alternans Detection in Ambulatory ECG via Time Analysis.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3553789

Carmen Plaza-Seco, Mohammad Baksh, Kenneth E Barner, Manuel Blanco-Velasco

{"title":"DeepTWA-TM: Deep Learning T-Wave Alternans Detection in Ambulatory ECG via Time Analysis.","authors":"Carmen Plaza-Seco, Mohammad Baksh, Kenneth E Barner, Manuel Blanco-Velasco","doi":"10.1109/JBHI.2025.3553789","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3553789","url":null,"abstract":"The development of non-invasive markers for assessing the risk of sudden cardiac death has gained significant attention, particularly T-wave alternans (TWA), which can be recorded from surface electrocardiogram (ECG) signals. However, the clinical application of TWA remains insufficiently standardized, complicating its detection in real-world ambulatory environments due to variable conditions that often affect ECG recordings, including dynamic changes, noise, and artifacts. This study presents a Deep Learning (DL) approach designed to detect TWA directly from ECG signals, using transfer learning with robust architectures such as VGG, ResNet, and Inception. Our method simplifies the detection pipeline by eliminating the need for prior signal processing steps such as R-peak identification, T-wave segmentation, or feature engineering. Our models are trained on a custom long-term dataset of real patients, capturing TWA episodes ranging from non-visible micro-alternans to higher amplitude TWA of 20 to 100 V, and incorporating a robust methodology that emphasizes patient separation during training and testing to enhance generalizability. The results demonstrate that our model achieves an F1-score of during ambulatory analysis, outperforming traditional Machine Learning approaches. By eliminating the need for extensive preprocessing, our approach not only enhances the adaptability of TWA detection but also brings the model closer to practical applicability in clinical settings, leading to more efficient and effective risk stratification for sudden cardiac death.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ExSMART-PreRA: Explainable Survival and Risk Assessment Using Machine Learning for Time Estimation in Preclinical Rheumatoid Arthritis.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3554364

Fatemeh Salehi, Sara Bayat, Georg Schett, Arnd Kleyer, Thomas Altstidl, Bjoern M Eskofier

{"title":"ExSMART-PreRA: Explainable Survival and Risk Assessment Using Machine Learning for Time Estimation in Preclinical Rheumatoid Arthritis.","authors":"Fatemeh Salehi, Sara Bayat, Georg Schett, Arnd Kleyer, Thomas Altstidl, Bjoern M Eskofier","doi":"10.1109/JBHI.2025.3554364","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3554364","url":null,"abstract":"Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease affecting peripheral joints. Before clinical diagnosis, individuals may possess certain antibodies and experience discomfort but without specific signs of RA or inflamed joints. This stage is termed \"preclinical RA,\" as these individuals are at risk of developing the disease. This early stage is difficult to define, necessitating the development of individual risk models. This study aims to estimate the time and risk of RA onset using various survival machine learning models. After identifying the best model, we stratify patients into risk categories and identify key risk factors. Data from 154 anonymized preclinical RA patients were collected and analyzed. Several survival analysis models were evaluated, including Survival Tree, Random Survival Forest, Extreme Gradient Boosting Survival, Linear Multi-Task Model, Neural Multi-Task Model, Support Vector Machines, and Cox Proportional Hazards. The Random Survival Forest model outperformed the others, achieving a mean C-index of 0.798. Using this model, patients were stratified into low-, medium-, and high-risk groups, facilitating personalized scheduling of clinical visits based on RA risk. To enhance model interpretability, SHapley Additive Explanations (SHAP) are employed to identify key risk factors. The baseline level of rheumatoid factor (RF) antibodies is the most significant predictor. Higher levels of anti-cyclic citrullinated peptide (anti-CCP) and RF antibodies at baseline are linked to earlier RA onset. This method provides valuable insights into key factors that might be overlooked in clinical practice and can improve patient management and quality of life for those at risk of developing RA.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MBBo-RPSLD: Training a Multimodal BlenderBot for Rehabilitation in Post-Stroke Language Disorder.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3554331

Yangyang Guo, Airu Huang, Bo Peng, Yufeng Li, Wei Gu

{"title":"MBBo-RPSLD: Training a Multimodal BlenderBot for Rehabilitation in Post-Stroke Language Disorder.","authors":"Yangyang Guo, Airu Huang, Bo Peng, Yufeng Li, Wei Gu","doi":"10.1109/JBHI.2025.3554331","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3554331","url":null,"abstract":"Stroke, a severe cerebrovascular event, can lead to motor deficits and often impairs language, affecting quality of life. Thus, developing effective rehabilitation models is crucial for enhancing language function and well-being in stroke patients. This paper presents the Multi-Blender model, designed to address the challenges of multimodal data processing and the complexity of medical dialogue in stroke language rehabilitation. The model integrates the multimodal encoding capabilities of ImageBind-LLM with the conversational generation strengths of BlenderBot, creating a tailored rehabilitation solution for stroke patients. We evaluated the model using a range of datasets, including the NINDS dataset, MSDM database, and clinical data from hospitals, focusing on audio-video recognition and speech translation tasks. Our results demonstrate that the Multi-Blender model outperforms existing models, achieving a BLEU score of 30.2 in the AST task, surpassing Whisper Large-v2 and AudioPaLM. In the ASR task, it also displayed superior performance. The model's effectiveness was further validated through an adjusted MME benchmark, where it scored 85.25% in perceptual tasks and 76.83% in cognitive tasks, outperforming other models in language understanding and fluency scoring. These findings indicate that the Multi-Blender model significantly enhances stroke language rehabilitation by improving multimodal data processing and providing accurate, reliable solutions. Future work will focus on expanding the training dataset and optimizing the model to further advance the effectiveness of stroke rehabilitation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MORPSO_ECD+ELM: A Unified Framework for Gene Selection and Cancer Classification.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3526825

Sumet Mehta, Fei Han, Qinghua Ling, Muhammad Sohail, Arfan Nagra

{"title":"MORPSO_ECD+ELM: A Unified Framework for Gene Selection and Cancer Classification.","authors":"Sumet Mehta, Fei Han, Qinghua Ling, Muhammad Sohail, Arfan Nagra","doi":"10.1109/JBHI.2025.3526825","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3526825","url":null,"abstract":"Gene selection and cancer classification are inherently multi-objective tasks that require balancing competing objectives, such as maximizing classification accuracy while minimizing irrelevant or redundant genes. Existing methods often optimize a single objective or treat gene selection and classification independently, limiting their overall effectiveness. This study proposes a unified framework, MORPSO_ECD+ELM, which formulates gene selection and classification as a multimodal multi-objective optimization problem (MMOP) to optimize both objectives simultaneously. The framework introduces two key innovations: (1) an enhanced crowding distance (ECD) metric to improve diversity preservation and (2) an advanced multi-objective particle swarm optimization variant (MORPSO_ECD) that incorporates ECD and ring topography to effectively explore the MMOP solution space. Integrated with the Extreme Learning Machine (ELM), this framework achieves robust and efficient cancer classification. Extensive experimental validations demonstrate that the proposed approach achieves high classification accuracy while identifying biologically meaningful gene subsets, providing a powerful solution to bridge the gap between gene selection and cancer classification.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VLD-Net: Localization and Detection of the Vertebrae from X-ray Images by Reinforcement Learning with Adaptive Exploration Mechanism and Spine Anatomy Information.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3553935

Shun Xiang, Lei Zhang, Yuanquan Wang, Shoujun Zhou, Xing Zhao, Tao Zhang, Shuo Li

{"title":"VLD-Net: Localization and Detection of the Vertebrae from X-ray Images by Reinforcement Learning with Adaptive Exploration Mechanism and Spine Anatomy Information.","authors":"Shun Xiang, Lei Zhang, Yuanquan Wang, Shoujun Zhou, Xing Zhao, Tao Zhang, Shuo Li","doi":"10.1109/JBHI.2025.3553935","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3553935","url":null,"abstract":"Accurate and efficient vertebrae localization and detection in X-ray images are essential for diagnosing and treating spinal diseases. However, most existing methods struggle with the complexity of spine X-ray images, yielding inaccurate results due to insufficient utilization of spinal anatomy information and neglect of individual vertebra characteristics. In this paper, we propose an innovative Vertebrae Localization and Detection Network (VLD-Net) to accurately assist physicians in diagnosing spine-related diseases from X-ray images. Our VLD-Net, for the first time, defines vertebrae localization as a top-bottom sequential decision-making process, employing deep reinforcement learning (DRL) to fully leverage the anatomical information of the spine. Simultaneously, it also prioritizes the distinct characteristics of each vertebra for accurate detection. Specifically, VLD-Net combines three key components: (1) An advanced vertebrae localization module based on DRL is proposed, effectively leveraging anatomical information of the spine. (2) A novel adaptive exploration mechanism is coined to understand the behavior of the DRL agent during training, pinpointing how to effectively achieve the trade-off between exploration and exploitation. (3) An innovative vertebra-focused module is proposed to accurately detect vertebral landmarks, using the attention region of each vertebra as input to enhance focus on the target and reduce interference from surrounding tissue. Extensive experiments on two public spine datasets demonstrate that the VLD-Net outperforms the state-of-the-art methods in accuracy and robustness. Our code is available at https://github.com/hlyf-xs/VLD-Net.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revealing Herb-Symptom Associations and Mechanisms of Action in Protein Networks Using Subgraph Matching Learning.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3554520

Menglu Li, Yongkang Wang, Yujing Ni, Hui Xiong, Zhinan Mei, Wen Zhang

{"title":"Revealing Herb-Symptom Associations and Mechanisms of Action in Protein Networks Using Subgraph Matching Learning.","authors":"Menglu Li, Yongkang Wang, Yujing Ni, Hui Xiong, Zhinan Mei, Wen Zhang","doi":"10.1109/JBHI.2025.3554520","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3554520","url":null,"abstract":"In traditional Chinese medicine, deciphering herb-symptom associations (HSAs) and revealing their mechanisms of action are crucial for bridging traditional knowledge and modern biomedicine. While previous studies have investigated HSAs using protein-protein interaction (PPI)-based network medicine method, they often treat all proteins equally, failing to capture the heterogeneous contributions of individual proteins to HSAs. This limitation hinders their capacity to reveal the mechanisms of action. To address this challenge, we propose a subgraph matching learning method, GraphHSA, for HSA prediction. GraphHSA maps herbs and symptoms onto the PPI network to construct subgraphs. Then, GraphHSA utilizes an attention mechanism to compute the importance of each protein on the subgraph, and weighted aggregate protein information to generate herb/symptom embeddings. Subsequently, these embeddings are combined to model the matching relationship between herb and symptom subgraphs, enabling association prediction. Additionally, a dual-contrastive learning strategy is introduced to generate discriminative representations to enhance prediction. Experiments indicate that GraphHSA not only applies to individual herbs but also extends to compound formulations composed of multiple herbs. By capturing the dynamic interactions among their components, GraphHSA enables the identification of key biological targets and the elucidation of the mechanisms underlying their therapeutic efficacy.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-25 DOI: 10.1109/JBHI.2025.3554495

Renju Liu, Jianfei Shen, Yang Gu, Yiqiang Chen, Jiling Zhang, Qingyu Wu, Chenyang Xu, Feiyi Fan

{"title":"PhysCL: Knowledge-Aware Contrastive Learning of Physiological Signal Models for Cuff-Less Blood Pressure Estimation.","authors":"Renju Liu, Jianfei Shen, Yang Gu, Yiqiang Chen, Jiling Zhang, Qingyu Wu, Chenyang Xu, Feiyi Fan","doi":"10.1109/JBHI.2025.3554495","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3554495","url":null,"abstract":"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.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0