IEEE Transactions on Biomedical Engineering最新文献_第7页

Design and evaluation of a sensor-instrumented clutch mechanism for quasi-passive back exosuits. 设计和评估用于准被动式背部外宇航服的传感器检测离合器装置。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-13 DOI: 10.1109/TBME.2025.3540625

Paul R Slaughter, Shane T King, Cameron A Nurse, Chad C Ice, Michael Goldfarb, Karl E Zelik

{"title":"Design and evaluation of a sensor-instrumented clutch mechanism for quasi-passive back exosuits.","authors":"Paul R Slaughter, Shane T King, Cameron A Nurse, Chad C Ice, Michael Goldfarb, Karl E Zelik","doi":"10.1109/TBME.2025.3540625","DOIUrl":"https://doi.org/10.1109/TBME.2025.3540625","url":null,"abstract":"Objective: We designed, built, and evaluated a new sensor-instrumented clutch to expand the capabilities of quasi-passive back exos (exoskeletons and exosuits) to include force sensing, posture sensing, and versatile mode switching. Quasi-passive back exos provide workers with lifting assistance, which can reduce their back injury risk. Central to their design is a clutch mechanism that enables the exo to assist when engaged and be unobstructive when disengaged. However, current exo clutches can have limited sensing and control capabilities.Design and methods: We designed a new clutch that integrates an encoder, solenoid, inertial measurement unit, and microprocessor to estimate exo assistance, monitor posture, and switch between engaged and disengaged modes. To validate the new capabilities, 6 participants wore a back exo during stoop and squat tasks. Data from the clutch's encoder were used to estimate assistance and trunk-thigh flexion angle, then compared to motion analysis lab measurements.Results: The prototype estimated exo assistance with an average error of 8.8 N (0.9 Nm of lumbar torque) and trunk-thigh angle with an average error of 6.7°. This prototype also maintained the core capabilities of a quasi-passive exo by withstanding 350 N of force when the clutch was engaged, exerting 7-20 N when disengaged, and switching between clutch modes in 0.1 seconds.Conclusion: We demonstrated an instrumented clutch that enabled exo assistance and posture monitoring, and more versatile control options, in addition to providing back relief.Significance: This clutch increases the capabilities of quasi-passive back exos, opening new opportunities for exo research and applications.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541717","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

Two-Source Validation of Online Surface EMG Decomposition Using Progressive FastICA Peel-off. 利用渐进式 FastICA 剥离技术对在线表面肌电图分解进行双源验证。

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-13 DOI: 10.1109/TBME.2025.3538338

Haowen Zhao, Maoqi Chen, Yunfei Liu, Xiang Chen, Ping Zhou, Xu Zhang

{"title":"Two-Source Validation of Online Surface EMG Decomposition Using Progressive FastICA Peel-off.","authors":"Haowen Zhao, Maoqi Chen, Yunfei Liu, Xiang Chen, Ping Zhou, Xu Zhang","doi":"10.1109/TBME.2025.3538338","DOIUrl":"https://doi.org/10.1109/TBME.2025.3538338","url":null,"abstract":"Recently, great interests have been attracted on the online decomposition of surface electromyogram (SEMG) but current studies mainly performed validation on simulated EMG signals due to the fact that real MU activities in experimental signals were unknown. For a more comprehensive assessment of online SEMG decomposition, a two-source validation was conducted by simultaneously collecting intramuscular EMG (IEMG) and high-density SEMG signals. The IEMG signal was decomposed using a simplified version of Progressive FastICA Peel-off (PFP) method with a combination of the peel-off strategy and the valley-seeking clustering, and the decomposed motor unit (MU) spike trains were used as the ground-truth reference. For SEMG recordings, the signals within initial 5 seconds were used to offline obtain MU separation vectors and these vectors were subsequently employed to extract MU spike trains in the online stage. The matching rate of the common firing events from the ground-truth reference and online SEMG decomposition were calculated and assessed. A total of 549 and 92 MUs were identified from the SEMG and IEMG signals from 5 healthy subjects' first dorsal interosseous muscle. All the MUs decomposed from IEMG can be matched with MUs from online SEMG decomposition and the average matching rate in the online stage was (96 ± 1) %. The results highlighted the ability of separation vectors to continuously and precisely track the same MU in the experimental SEMG signals. Our study provides a more comprehensive validation perspective of online SEMG decomposition on the experimental data.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541709","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

A Novel Mutual Information-based Approach for Neurophysiological Characterization of Sense of Presence in Virtual Reality. 基于互信息的新方法：虚拟现实中临场感的神经生理学特征描述

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-13 DOI: 10.1109/TBME.2025.3541438

Vincenzo Ronca, Fabio Babiloni, Pietro Arico

{"title":"A Novel Mutual Information-based Approach for Neurophysiological Characterization of Sense of Presence in Virtual Reality.","authors":"Vincenzo Ronca, Fabio Babiloni, Pietro Arico","doi":"10.1109/TBME.2025.3541438","DOIUrl":"https://doi.org/10.1109/TBME.2025.3541438","url":null,"abstract":"Objective: The presented work aimed to investigate neurophysiological markers of sense of presence in virtual reality. The study was based on developing and preliminary validating a neurophysiological -based approach for sense of presence evaluation.Methods: A VR environment was designed to modulate multisensory conditions, including visual, auditory, vibrotactile stimuli. EEG, ECG, EDA signals were recorded. The Mutual Information-based sense of presence index (SoPMI) was developed as a synthetic metric for sense of presence, integrating multiple physiological signals. Signal preprocessing and analysis were conducted using EEG-based Global Field Power and Skin Conductance Level to explore their relationship with sense of presence under different VR conditions.Results: SoPMI index demonstrated sensitivity to varying levels of multisensory integration and immersion (all p < 0.001). EEG-derived features, particularly in theta and alpha bands, were highly correlated with subjective sense of presence scores (R = 0.559, p < 0.007). Additionally, autonomic markers, such as skin conductance, showed strong associations with engagement, particularly under high-immersion conditions.Conclusion: The study successfully identified neurophysiological markers of sense of presence and preliminarily validated the SoPMI index as a potential objective measure for VR applications. These findings establish foundation for reliable and immersive VR experiences across fields, including training, rehabilitation and industry 5.0.Significance: By providing an objective and multimodal framework for measuring sense of presence, this research contributes to advancing VR applications where the sense of presence accurate and reliable assessment is essential. The SoPMI index offers potential for enhancing VR design and creating more effective, user-centered immersive experiences.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541776","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

Passive Beamforming Metasurfaces for Microwave-induced Thermoacoustic Imaging.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-13 DOI: 10.1109/TBME.2025.3541252

Shuangfeng Tang, Yichao Fu, Yu Wang, Xiaoyu Tang, Lizhang Zeng, Huan Qin

{"title":"Passive Beamforming Metasurfaces for Microwave-induced Thermoacoustic Imaging.","authors":"Shuangfeng Tang, Yichao Fu, Yu Wang, Xiaoyu Tang, Lizhang Zeng, Huan Qin","doi":"10.1109/TBME.2025.3541252","DOIUrl":"https://doi.org/10.1109/TBME.2025.3541252","url":null,"abstract":"Objective: Microwave-induced thermoacoustic imaging (MTAI) responds to the electromagnetic properties of biological tissues, providing non-ionizing, high-resolution, and deep-penetration imaging, with significant potential for clinical diagnostics and treatment. However, the current MTAI faces issues of reduced signal-to-noise ratio (SNR) and contrast when imaging deep tissues.Methods: In this study, we propose a passive beamforming metasurface (PB-MS) (270 mm × 270 mm × 5 mm), designed to focus microwave energy on deeper regions using phase control, enabling more sensitive MTAI of deep tissues. The PB-MS is composed of 27 superstructure units, which generate surface plasmons when excited by microwave fields. By arranging these units, the microwave field is reshaped to focus and distribute evenly, increasing the energy density in target areas. This enhances thermoacoustic signals, improving the imaging SNR and contrast.Results: Both simulations and experiments were conducted to evaluate the practical feasibility of MTAI with PB-MS. The results showed that with PB-MS, the SNR remained as high as 22.2 dB in muscle phantoms at a depth of 7.5 cm. The MTAI system, equipped with PB-MS, is capable of detecting a minimum conductivity change of 0.095 S/m and identifying micro-liter level hemorrhages in a mouse model of hemorrhagic stroke.Conclusion: These results demonstrate that PB-MS optimizes energy delivery in MTAI, enabling deeper and more sensitive imaging.Significance: PB-MS effectively enhances MTAI imaging quality, representing a critical step toward its clinical application.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541035","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

Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-13 DOI: 10.1109/TBME.2025.3542253

Miguel A Lopez-Gordo, Simon Geirnaert, Alexander Bertrand

{"title":"Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding.","authors":"Miguel A Lopez-Gordo, Simon Geirnaert, Alexander Bertrand","doi":"10.1109/TBME.2025.3542253","DOIUrl":"https://doi.org/10.1109/TBME.2025.3542253","url":null,"abstract":"Objective: Selective auditory attention decoding (AAD) algorithms process brain data such as electroencephalography to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or communication via brain-computer interfaces (BCI). Recently, it has been shown that it is possible to train such AAD decoders based on stimulus reconstruction in an unsupervised setting, where no ground truth is available regarding which sound source is attended. In many practical scenarios, such ground-truth labels are absent, making it, moreover, difficult to quantify the accuracy of the decoders. In this paper, we aim to develop a completely unsupervised algorithm to estimate the accuracy of correlation-based AAD algorithms during a competing talker listening task.Methods: We use principles of digital communications by modeling the AAD decision system as a binary phase-shift keying channel with additive white gaussian noise.Results: We show that the proposed unsupervised performance estimation technique can accurately determine the AAD accuracy in a transparent-for-the-user way, for different amounts of training and estimation data and decision window lengths. Furthermore, since different applications demand different targeted accuracies, our approach can estimate the minimal amount of training required for any given target accuracy.Conclusion: Our proposed estimation technique accurately predicts the performance of a correlation-based AAD algorithm without access to ground-truth labels.Significance: In neuro-steered hearing aids, the accuracy estimates provided by our approach could support time-adaptive decoding, dynamic gain control, and neurofeedback. In BCIs, it could support a robust communication paradigm with accuracy feedback for caregivers.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541725","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

Accelerated Simulation of Multi-Electrode Arrays Using Sparse and Low-Rank Matrix Techniques.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-13 DOI: 10.1109/TBME.2025.3541489

Nathan Jensen, Zhijie Charles Chen, Anna Kochnev Goldstein, Daniel Palanker

{"title":"Accelerated Simulation of Multi-Electrode Arrays Using Sparse and Low-Rank Matrix Techniques.","authors":"Nathan Jensen, Zhijie Charles Chen, Anna Kochnev Goldstein, Daniel Palanker","doi":"10.1109/TBME.2025.3541489","DOIUrl":"10.1109/TBME.2025.3541489","url":null,"abstract":"Objective: Modeling of Multi-Electrode Arrays used in neural stimulation can be computationally challenging since it may involve incredibly dense circuits with millions of interconnected resistors, representing current pathways in an electrolyte (resistance matrix), coupled to nonlinear circuits of the stimulating pixels themselves. Here, we present a method for accelerating the modeling of such circuits with minimal error by using a sparse plus low-rank approximation of the resistance matrix.Methods: We prove that thresholding of the resistance matrix elements enables its sparsification with minimized error. This is accomplished with a sorting algorithm, implying efficient O (N log (N)) complexity. The eigenvalue-based low-rank compensation then helps achieve greater accuracy without significantly increasing the problem size.Results: Utilizing these matrix techniques, we reduced the computation time of the simulation of multi-electrode arrays by about 10-fold, while maintaining an average error of less than 0.3% in the current injected from each electrode. We also show a case where acceleration reaches at least 133 times with additional error in the range of 4%, demonstrating the ability of this algorithm to perform under extreme conditions.Conclusion: Critical improvements in the efficiency of simulations of the electric field generated by multi-electrode arrays presented here enable the computational modeling of high-fidelity neural implants with thousands of pixels, previously impossible.Significance: Computational acceleration techniques described in this manuscript were developed for simulation of high-resolution photovoltaic retinal prostheses, but they are also immediately applicable to any circuits involving dense connections between nodes, and, with modifications, more generally to any systems involving non-sparse matrices.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541053","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

Modified Feature Selection for Improved Classification of Resting-State Raw EEG Signals in Chronic Knee Pain. 改进慢性膝关节疼痛静息态原始脑电信号分类的修正特征选择

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-07 DOI: 10.1109/TBME.2024.3517659

Jean Li, Dirk De Ridder, Divya Adhia, Matthew Hall, Ramakrishnan Mani, Jeremiah D Deng

{"title":"Modified Feature Selection for Improved Classification of Resting-State Raw EEG Signals in Chronic Knee Pain.","authors":"Jean Li, Dirk De Ridder, Divya Adhia, Matthew Hall, Ramakrishnan Mani, Jeremiah D Deng","doi":"10.1109/TBME.2024.3517659","DOIUrl":"https://doi.org/10.1109/TBME.2024.3517659","url":null,"abstract":"Objective: Diagnosing pain in research and clinical practices still relies on self-report. This study aims to develop an automatic approach that works on resting-state raw EEG data for chronic knee pain prediction.Method: A new feature selection algorithm called \"modified Sequential Floating Forward Selection\" (mSFFS) is proposed. The improved feature selection scheme can better avoid local minima andexplore alternative search routes.Results: The feature selection obtained by mSFFS displays better class separability as indicated by the Bhattacharyya distance measures and better visualization results. It also outperforms selections generated by other benchmark methods, boosting the test accuracy to 97.5%.Conclusion: The improved feature selection searches out a compact, effective subset of connectivity features that produces competitive performance on chronic knee pain prediction.Significance: We have shown that an automatic approach can be employed to find a compact connectivity feature set that effectively predicts chronic knee pain from EEG. It may shed light on the research of chronic pains and lead to future clinical solutions for diagnosis and treatment.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541766","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

Automation and Control of Embryo Trophectoderm Cell Biopsy at the Blastocyst Stage.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-07 DOI: 10.1109/TBME.2025.3532886

Ihab Abu Ajamieh, Mohammad Al Saaideh, Mohammad Al Janaideh, James K Mills

{"title":"Automation and Control of Embryo Trophectoderm Cell Biopsy at the Blastocyst Stage.","authors":"Ihab Abu Ajamieh, Mohammad Al Saaideh, Mohammad Al Janaideh, James K Mills","doi":"10.1109/TBME.2025.3532886","DOIUrl":"https://doi.org/10.1109/TBME.2025.3532886","url":null,"abstract":"Objective: This study aims to develop and validate a vision-based automation framework for performing trophectoderm (TE) cell biopsy on mouse embryos.Method: The proposed framework leverages widely available tools in research laboratories and In-Vitro fertilization (IVF) clinics, combined with computer vision and image-based control algorithms. A computer vision system first estimates the embryo's orientation to enable precise reorientation for zona pellucida (ZP) laser perforation. A vision-feedback control system then guides the embryo to the targeted perforation location and determines optimal laser parameters for ZP perforation. A vision-guided vacuum system aspirates the TE cells, with a multi-pulse laser ensuring their separation.Results: Experimental validation using mouse blastocyst embryos demonstrated the feasibility and reliability of the proposed automation method. The vision-based approach achieved accurate orientation, controlled ZP perforation, and successful isolation of TE cells, effectively replicating manual biopsy techniques performed by skilled embryologists.Conclusion: The study presents a robust framework for automating embryo TE biopsy, reducing variability, and enhancing procedural precision. Integrating computer vision and control algorithms allows for consistent and reproducible results.Significance: By utilizing existing infrastructure, the proposed method offers a cost-effective and scalable solution for single-cell research and IVF clinics, advancing genetic testing and reproductive medicine.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541682","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

Optimal Sensor Selection for Motion-Corrected Supine Breast MRI with a Wearable Coil.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-07 DOI: 10.1109/TBME.2025.3539386

Karyna Isaieva, Nicolas Weber, Lena Nohava, Barbara Fischer, Alexandre Megel, Philippe Henrot, Emilien Micard, Elmar Laistler, Jacques Felblinger, Freddy Odille

{"title":"Optimal Sensor Selection for Motion-Corrected Supine Breast MRI with a Wearable Coil.","authors":"Karyna Isaieva, Nicolas Weber, Lena Nohava, Barbara Fischer, Alexandre Megel, Philippe Henrot, Emilien Micard, Elmar Laistler, Jacques Felblinger, Freddy Odille","doi":"10.1109/TBME.2025.3539386","DOIUrl":"https://doi.org/10.1109/TBME.2025.3539386","url":null,"abstract":"Objective: Supine breast MRI offers several advantages over conventional prone MRI but requires correction of motion artifacts. This study aimed to identify the optimal combination of motion sensors for achieving the best motion correction quality using a wearable radio frequency coil (\"BraCoil\").Methods: T2-weighted (T2w) 2D and T1-weighted (T1w) 3D sequences were acquired in 10 volunteers and 17 exams. Images were reconstructed using the GRICS motion correction algorithm, using data from a respiratory belt or MRI-compatible accelerometers. The motion models were evaluated by comparing the predicted motion to dynamic MRI. The resulting images were also assessed using both quantitative metrics and radiological scoring. Additionally, the accuracy of motion models calculated from high-resolution 2D T2w images was compared to those derived from low-resolution 3D T1w images.Results: Motion predicted from 2D T2w images matched the dynamic MRI significantly better than that predicted from low-resolution 3D T1w images. Radiological scoring showed that using all accelerometers significantly improved image quality compared to using the belt. A single accelerometer explained only 45-70% of the total sensor data, and combining all sensors led to superior correction overall. No optimal sensor position was identified.Conclusion: The motion model calculated from 2D T2w images, using the data from all accelerometers, provides the best motion correction quality for supine breast MRI using a wearable coil.Significance: Selecting the appropriate physiological sensors minimizes motion artifacts and can potentially enhance diagnostic accuracy.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541778","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

Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-02-06 DOI: 10.1109/TBME.2025.3539498

Ruige Zong, Tao Wang, Chunwang Li, Xinlin Zhang, Yuanbin Chen, Longxuan Zhao, Qixuan Li, Qinquan Gao, Dezhi Kang, Fuxin Lin, Tong Tong

{"title":"Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations.","authors":"Ruige Zong, Tao Wang, Chunwang Li, Xinlin Zhang, Yuanbin Chen, Longxuan Zhao, Qixuan Li, Qinquan Gao, Dezhi Kang, Fuxin Lin, Tong Tong","doi":"10.1109/TBME.2025.3539498","DOIUrl":"https://doi.org/10.1109/TBME.2025.3539498","url":null,"abstract":"Familial cerebral cavernous malformation (FCCM) is a hereditary disorder characterized by abnormal vascular structures within the central nervous system. The FCCM lesions are often numerous and intricate, making quantitative analysis of the lesions a labor-intensive task. Consequently, clinicians face challenges in quantitatively assessing the severity of lesions and determining whether lesions have progressed. To alleviate this problem, we propose a quantitative statistical framework for FCCM, which comprises an efficient annotation module, an FCCM lesion segmentation module, and an FCCM lesion quantitative statistics module. Our framework demonstrates precise segmentation of the FCCM lesion based on efficient data annotation, achieving a Dice coefficient of 91.09%. More importantly, we focus on 3D quantitative statistics of lesions, which is combined with image registration to realize the quantitative comparison of lesions between different examinations of patients. A visualization framework has also been established for doctors to comprehensively compare and analyze lesions. The experimental results have demonstrated that our proposed framework not only obtains objective, accurate, and comprehensive quantitative statistical information, which provides a quantitative assessment method for disease progression and drug efficacy study, but also considerably reduces the manual measurement and statistical workload of lesions. This highlights the potential of practical application of the framework in FCCM clinical research and clinical decision-making.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143541753","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