IEEE Transactions on Biomedical Engineering最新文献_第3页

GZSL-Lite: A Lightweight Generalized Zero-Shot Learning Network for SSVEP-Based BCIs.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1109/TBME.2025.3553204

Xietian Wang, Aiping Liu, Heng Cui, Xingui Chen, Kai Wang, Xun Chen

{"title":"GZSL-Lite: A Lightweight Generalized Zero-Shot Learning Network for SSVEP-Based BCIs.","authors":"Xietian Wang, Aiping Liu, Heng Cui, Xingui Chen, Kai Wang, Xun Chen","doi":"10.1109/TBME.2025.3553204","DOIUrl":"10.1109/TBME.2025.3553204","url":null,"abstract":"Generalized zero-shot learning (GZSL) networks offer promising avenues for the development of user-friendly steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs), aiming to alleviate the training burden on users. These networks only require the user to provide training data from partial classes during training, yet they demonstrate the capability to classify all classes during testing. However, these GZSL networks have a large number of trainable parameters, resulting in long training times and difficulty to practicalize. In this study, we proposed a dual-attention structure to construct a lightweight GZSL network, termed GZSL-Lite. We first embedded the input training data-constructed class templates, manually constructed sine templates, and electroencephalogram (EEG) signals using convolution-based networks. The embedding part uses the same network weights to embed the features across different stimulus frequencies while reducing the depth of the network. After embedding, two branches of the dual-attention use class and sine templates to guide the feature extraction of the EEG signal with the attention mechanism, respectively. Compared to the networks that extract all features equally, dual-attention focuses only on EEG features relative to templates, which helps classification with fewer parameters. Finally, we used depthwise convolutional blocks to output classification results. Experimental evaluations conducted on two publicly available datasets demonstrate the efficacy of the proposed network. Comparative analysis reveals a remarkable reduction in trainable parameters to less than 1% of the SOTA counterpart, concurrently showing significant performance improvement. The code is available for reproducibility at https://github.com/xtwong111/GZSL-Lite-for-SSVEP-Based-BCIs.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669732","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

Population-Driven Synthesis of Personalized Cranial Development from Cross-Sectional Pediatric CT Images.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-18 DOI: 10.1109/TBME.2025.3550842

Jiawei Liu, Fuyong Xing, Connor Elkhill, Marius George Linguraru, Randy C Miles, Ines A Cruz-Guerrero, Antonio R Porras

{"title":"Population-Driven Synthesis of Personalized Cranial Development from Cross-Sectional Pediatric CT Images.","authors":"Jiawei Liu, Fuyong Xing, Connor Elkhill, Marius George Linguraru, Randy C Miles, Ines A Cruz-Guerrero, Antonio R Porras","doi":"10.1109/TBME.2025.3550842","DOIUrl":"10.1109/TBME.2025.3550842","url":null,"abstract":"Objective: Predicting normative pediatric growth is crucial to identify developmental anomalies. While traditional statistical and computational methods have shown promising results predicting personalized development, they either rely on statistical assumptions that limit generalizability or require longitudinal datasets, which are scarce in children. Recent deep learning methods trained with cross-sectional dataset have shown potential to predict temporal changes but have only succeeded at predicting local intensity changes and can hardly model major anatomical changes that occur during childhood. We present a novel deep learning method for image synthesis that can be trained using only cross-sectional data to make personalized predictions of pediatric development.Methods: We designed a new generative adversarial network (GAN) with a novel Siamese cyclic encoder-decoder generator architecture and an identity preservation mechanism. Our design allows the encoder to learn age- and sex-independent identity-preserving representations of patient phenotypes from single images by leveraging the statistical distributions in the cross-sectional dataset. The decoder learns to synthesize personalized images from the encoded representations at any age.Results: Trained using only cross-sectional head CT images from 2,014 subjects (age 0-10 years), our model demonstrated state-of-the-art performance evaluated on an independent longitudinal dataset with images from 51 subjects.Conclusion: Our method can predict pediatric development and synthesize temporal image sequences with state-of-the-art accuracy without requiring longitudinal images for training.Significance: Our method enables the personalized prediction of pediatric growth and longitudinal synthesis of clinical images, hence providing a patient-specific reference of normative development.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656923","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 Seismocardiogram Based Monitoring of Left Ventricular Ejection Time.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-18 DOI: 10.1109/TBME.2025.3548090

Zhiqin Zhou, Jia Huang, Haozhe Li, Lin Liu, Yingen Zhu, Caifeng Shan, Wenjin Wang

{"title":"Camera Seismocardiogram Based Monitoring of Left Ventricular Ejection Time.","authors":"Zhiqin Zhou, Jia Huang, Haozhe Li, Lin Liu, Yingen Zhu, Caifeng Shan, Wenjin Wang","doi":"10.1109/TBME.2025.3548090","DOIUrl":"10.1109/TBME.2025.3548090","url":null,"abstract":"Left Ventricular Ejection Time (LVET), reflecting the duration from the onset to the end of blood ejection by the left ventricle during each heartbeat, is a critical parameter for measuring cardiac pumping efficiency. Continuous and regular monitoring of LVET is particularly crucial in assessing cardiac health, valvular function, and myocardial contractility. Seismocardiogram (SCG) signals can be utilized for LVET monitoring, as the temporal distance between the aortic valve opening (AO) and aortic valve closure (AC) in SCG signals can accurately depict LVET. This study proposes a novel way to extract LVET from laser speckle videos recorded by a remote camera based on the principle of defocused speckle imaging, thereby enabling non-contact monitoring of LVET. We extract both the low-frequency components of laser speckle motion (LSM-LF), regarded as SCG signals, and the high-frequency components of laser speckle motion (LSM-HF) from recorded videos. We utilize LSM-HF to assist the detection of AO and AC markers in LSM-LF. We validated the effectiveness of our AO and AC detection algorithm on a self-made dataset comprising 21 participants with 9616 SCG cycles. The benchmark shows that the detection accuracy for AO and AC reached 98.16% and 97.94%, respectively, with an mean absolute error of 0.5571 ms for LVET estimation. The results demonstrate that camera-SCG has strong potential for cardiac health monitoring.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656833","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

In Vivo Characterization of Central Arterial Properties Using a Miniaturized pMUT Array Compared to a Clinical Transducer: A Feasibility Study Towards Wearable Pulse Wave Imaging. 与临床传感器相比，使用微型 pMUT 阵列对中心动脉特性进行体内表征：实现穿戴式脉冲波成像的可行性研究

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-14 DOI: 10.1109/TBME.2025.3551281

Parth Gami, Tuhin Roy, Pengcheng Liang, Paul Kemper, Marco Travagliati, Leonardo Baldasarre, Stephen Bart, Elisa E Konofagou

{"title":"In Vivo Characterization of Central Arterial Properties Using a Miniaturized pMUT Array Compared to a Clinical Transducer: A Feasibility Study Towards Wearable Pulse Wave Imaging.","authors":"Parth Gami, Tuhin Roy, Pengcheng Liang, Paul Kemper, Marco Travagliati, Leonardo Baldasarre, Stephen Bart, Elisa E Konofagou","doi":"10.1109/TBME.2025.3551281","DOIUrl":"10.1109/TBME.2025.3551281","url":null,"abstract":"Objective: Piezoelectric micromachined ultrasound transducer (pMUT) technology shows promise for wearable ultrasound applications, although with limitations in acquisition performance compared to standard transducers. To translate Pulse Wave Imaging (PWI)-an ultrasound imaging technique that evaluates local arterial mechanics-into wearable applications, this study investigated the performance of integrating a miniaturized pMUT array into the PWI pipeline.Methods: Nine (n = 9) carotid arteries were scanned with a miniaturized pMUT array and an L7-4 linear transducer. Metrics like pulse wave velocity at end-diastole (PWVED) and end-systole (PWVES), compliance (CED, CES), and carotid pulse pressure (PPC) were compared between imaging arrays.Results: Lower SNR of axial wall velocities (SNRvPWI) at end-diastole (L7-4: 47.9 ± 6.8 dB, pMUT: 43.3 ± 7.4 dB) and end-systole (L7-4: 45.4 ± 6.4 dB, pMUT: 38.1 ± 6.5 dB), and trends of higher coefficient of variation (CV) were found for PWI performed with the pMUT array compared to the L7-4. Bland-Altman analysis identified good agreement between the L7-4 and pMUT array for average PWVED (bias = -0.02 ± 0.42 m/s), PWVES (bias = -0.38 ± 1.3 m/s), CED (bias = 0.04 x 10-9 ± 0.24 x 10-9 m2/Pa), CES (bias = 0.11 x 10-9 ± 0.38 x 10-9 m2/Pa) and PPC (bias = 1.06 ± 5.08 mmHg).Conclusion: The findings revealed comparable performance between the miniaturized pMUT array and L7-4 for PWI, highlighting the versatility of the PWI technique.Significance: This feasibility study illustrates the potential for translating PWI into wearable configurations, opening new avenues for cardiovascular health monitoring.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630253","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

Evaluation of a Low-Cost Amplifier With System Optimization in Thermoacoustic Tomography: Characterization and Imaging of Ex-Vivo and In-Vivo Samples.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-14 DOI: 10.1109/TBME.2025.3551260

Md Tarikul Islam, Mohsin Zafar, Ravi Prakash, Deepika Aggrawal, Danilo Erricolo, James Lin, Kamran Avanaki

{"title":"Evaluation of a Low-Cost Amplifier With System Optimization in Thermoacoustic Tomography: Characterization and Imaging of Ex-Vivo and In-Vivo Samples.","authors":"Md Tarikul Islam, Mohsin Zafar, Ravi Prakash, Deepika Aggrawal, Danilo Erricolo, James Lin, Kamran Avanaki","doi":"10.1109/TBME.2025.3551260","DOIUrl":"10.1109/TBME.2025.3551260","url":null,"abstract":"Microwave-induced thermoacoustic tomography (TAT) is a hybrid imaging technique that combines microwave excitation with ultrasound detection to create detailed images of biological tissue. Most TAT systems require a costly amplification system (or a sophisticated high-power microwave source), which limits the wide adoption of this imaging modality. We have developed a rotating single-element thermoacoustic tomography (RTAT) system using a low-cost amplifier that has been optimized in terms of microwave signal pulse width and antenna placement. The optimized system, enhanced with signal averaging, advanced signal processing, and a deep learning computational core, successfully produced adequate-quality images. The system has been characterized in terms of spatial resolution, imaging depth, acquisition speed, and multispectral capabilities utilizing tissue-like phantoms, ex-vivo specimens and in-vivo imaging. We believe our low-cost, portable system expands accessibility for the research community, empowering more groups to explore thermoacoustic imaging. It supports the development of advanced signal processing algorithms to optimize both low-power and even high-power TAT systems, accelerating the clinical adoption of this promising imaging modality.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630249","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

Semi-Autonomous Laparoscopic Robot Docking with Learned Hand-Eye Information Fusion.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-13 DOI: 10.1109/TBME.2025.3550974

Huanyu Tian, Martin Huber, Christopher E Mower, Zhe Han, Changsheng Li, Xingguang Duan, Christos Bergeles

{"title":"Semi-Autonomous Laparoscopic Robot Docking with Learned Hand-Eye Information Fusion.","authors":"Huanyu Tian, Martin Huber, Christopher E Mower, Zhe Han, Changsheng Li, Xingguang Duan, Christos Bergeles","doi":"10.1109/TBME.2025.3550974","DOIUrl":"10.1109/TBME.2025.3550974","url":null,"abstract":"In this study, we introduce a novel shared-control system for key-hole docking operations, combining a commercial camera with occlusion-robust pose estimation and a hand-eye information fusion technique. This system is used to enhance docking precision and force-compliance safety. To train a hand-eye information fusion network model, we generated a self-supervised dataset using this docking system. After training, our pose estimation method showed improved accuracy compared to traditional methods, including observation-only approaches, hand-eye calibration, and conventional state estimation filters. In real-world phantom experiments, our approach demonstrated its effectiveness with reduced position dispersion (1.230.81 mm vs. 2.47 1.22 mm) and force dispersion (0.780.57 N vs. 1.150.97 N) compared to the control group. These advancements in semi-autonomy co-manipulation scenarios enhance interaction and stability. The study presents an anti-interference, steady, and precise solution with potential applications extending beyond laparoscopic surgery to other minimally invasive procedures.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143624476","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

Time of Flight Transmission Mode Ultrasound Computed Tomography with Expected Gradient and Boundary Optimization.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-12 DOI: 10.1109/TBME.2025.3550823

Roberto C Ceccato, Andre V Pigatto, Richard C Aster, Chi-Nan Pai, Jennifer L Mueller, Sergio S Furuie

{"title":"Time of Flight Transmission Mode Ultrasound Computed Tomography with Expected Gradient and Boundary Optimization.","authors":"Roberto C Ceccato, Andre V Pigatto, Richard C Aster, Chi-Nan Pai, Jennifer L Mueller, Sergio S Furuie","doi":"10.1109/TBME.2025.3550823","DOIUrl":"https://doi.org/10.1109/TBME.2025.3550823","url":null,"abstract":"Objective: Quantitative time of flight in transmission mode ultrasound computed tomography (TFTM USCT) is a promising, cost-effective, and non-invasive modality, particularly suited for functional imaging. However, TFTM USCT encounters resolution challenges due to path information concentration in specific medium regions and uncertainty in transducer positioning. This study proposes a method to enhance resolution and robustness, focusing on low-frequency TFTM USCT for pulmonary imaging.Methods: The proposed technique improves the orientation of steepest descent algorithm steps, preventing resolution degradation due to path information concentration, while allowing for a posteriori sensor positioning retrieval. Total variation regularization is employed to stabilize the inverse problem, and a modified Barzilai-Borwein method determined the step size in the steepest descent algorithm. The proposed method was validated through simulations of data on healthy and abnormal cross-sections of a human chest using MATLAB's k-Wave toolbox. Additionally, experimental data were collected using a Verasonics Vantage 64 low-frequency system and a ballistic gel torso-mimicking phantom to assess robustness under a more realistic environment, closer to that of a clinical situation.Results: The results showed that the proposed method significantly improved image quality and successfully retrieved sensor locations from imprecise positioning.Significance: This study is the first to address transducer location uncertainty on a transducer belt in TFTM USCT and to apply an estimated gradient approach. Additionally, low-frequency USCT for lung imaging is quite novel, and this work addresses practical questions that will be important for translational development.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614795","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

Fast and Stable Neonatal Brain MR Imaging Using Integrated Learned Subspace Model and Deep Learning.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-12 DOI: 10.1109/TBME.2025.3541643

Ziwen Ke, Yue Guan, Tianyao Wang, Huixiang Zhuang, Zijun Cheng, Yunpeng Zhang, Jing-Ya Ren, Su-Zhen Dong, Yao Li

{"title":"Fast and Stable Neonatal Brain MR Imaging Using Integrated Learned Subspace Model and Deep Learning.","authors":"Ziwen Ke, Yue Guan, Tianyao Wang, Huixiang Zhuang, Zijun Cheng, Yunpeng Zhang, Jing-Ya Ren, Su-Zhen Dong, Yao Li","doi":"10.1109/TBME.2025.3541643","DOIUrl":"https://doi.org/10.1109/TBME.2025.3541643","url":null,"abstract":"Objective: To enable fast and stable neonatal brain MR imaging by integrating learned neonate-specific subspace model and model-driven deep learning.Methods: Fast data acquisition is critical for neonatal brain MRI, and deep learning has emerged as an effective tool to accelerate existing fast MRI methods by leveraging prior image information. However, deep learning often requires large amounts of training data to ensure stable image reconstruction, which is not currently available for neonatal MRI applications. In this work, we addressed this problem by utilizing a subspace model-assisted deep learning approach. Specifically, we used a subspace model to capture the spatial features of neonatal brain images. The learned neonate-specific subspace was then integrated with a deep network to reconstruct high-quality neonatal brain images from very sparse k-space data.Results: The effectiveness and robustness of the proposed method were validated using both the dHCP dataset and testing data from four independent medical centers, yielding very encouraging results. The stability of the proposed method has been confirmed with different perturbations, all showing remarkably stable reconstruction performance. The flexibility of the learned subspace was also shown when combined with other deep neural networks, yielding improved image reconstruction performance.Conclusion: Fast and stable neonatal brain MR imaging can be achieved using subspace-assisted deep learning with sparse sampling. With further development, the proposed method may improve the practical utility of MRI in neonatal imaging applications.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614778","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 NICU Sleep State Stratification: Multiperspective Features, Adaptive Feature Selection and Ensemble Model.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-11 DOI: 10.1109/TBME.2025.3549584

Muhammad Irfan, Abdulhamit Subasi, Zhenning Tang, Laishuan Wang, Yan Xu, Chen Chen, Tomi Westurlund, Wei Chen

{"title":"A Novel NICU Sleep State Stratification: Multiperspective Features, Adaptive Feature Selection and Ensemble Model.","authors":"Muhammad Irfan, Abdulhamit Subasi, Zhenning Tang, Laishuan Wang, Yan Xu, Chen Chen, Tomi Westurlund, Wei Chen","doi":"10.1109/TBME.2025.3549584","DOIUrl":"https://doi.org/10.1109/TBME.2025.3549584","url":null,"abstract":"The examination of sleep patterns in newborns, particularly premature infants, is crucial for understanding neonatal development. This study presents an automated multi-sleep state classification approach for infants in neonatal intensive care units (NICU) using multiperspective feature extraction methodologies and machine learning to assess their neurological and physical development. The datasets for this study were collected from Children's Hospital Fudan University, Shanghai and consist of electroencephalography (EEG) recordings from two datasets, one comprising 64 neonates and the other 19 neonates. The proposed study involves six major phases: data collection, data annotation, preprocessing, multi-perspective feature extraction, adaptive feature selection, and classification. During the preprocessing phase, noise reduction is achieved using the multi-scale principal component analysis (MSPCA) method. From each epoch of eight EEG channels, a diverse ensemble of 1,976 features is extracted. This extraction employs a combination of stationary wavelet transform (SWT), flexible analytical wavelet transform (FAWT), spectral features based on α, β, θ, and δ brain waves, and temporal features refined through adaptive feature algorithm. In terms of performance, the proposed approach demonstrates significant improvements over existing studies. Using a single EEG channel, the model achieves accuracy of 81.45% and a Kappa score of 71.75%. With four channels, these metrics increase to 83.71% accuracy and a 74.04% Kappa score. Furthermore, utilizing all eight channels, the mean accuracy reaches to 85.62%, and the Kappa score rises to 76.30%. To evaluate the model's effectiveness, a leave-one-subject-out cross-validation method is employed. This thorough analysis validates the reliability of the classification approach. This makes it a promising method for monitoring and assessing sleep patterns in neonates.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604640","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

Design of Magnetic Actuated Capsule Robot for Liquid Sampling.

IF 4.4 2区医学

IEEE Transactions on Biomedical Engineering Pub Date : 2025-03-11 DOI: 10.1109/TBME.2025.3550179

Shuo Zhang, Shaohui Song, Xinkai Yu, Shuang Song, Lihai Zhang

{"title":"Design of Magnetic Actuated Capsule Robot for Liquid Sampling.","authors":"Shuo Zhang, Shaohui Song, Xinkai Yu, Shuang Song, Lihai Zhang","doi":"10.1109/TBME.2025.3550179","DOIUrl":"10.1109/TBME.2025.3550179","url":null,"abstract":"Objective: This study aims to introduce a biopsy capsule robot based on the negative pressure suction principle to achieve liquid sampling in the digestive tract.Methods: The proposed capsule robot is designed with a magnetic spring configuration. By controlling the direction of an external magnetic field, the suction port can be aligned with the target sampling area. The sampling operation can then be achieved by increasing the external field to start the magnetic spring and a negative pressure can be generated to achieve the liquid sampling. Moreover, a locking mechanism is designed to prevent the magnetic spring from retracting, ensuring that the collected liquid is not squeezed out.Results: The capsule robot prototype has dimensions of 16.3mm × 24.4mm. Both phantom and in-vitro experiments have been carried out. Results showed that it can sample liquids with viscosities ranging from 0.7mPa s to 200mPa s and absorb up to 0.24ml liquid. Additionally, the sealing of the capsule also meets clinical requirements.Conclusion: The experimental results indicate that the designed capsule robot can satisfy the clinical requirements for liquid sampling within the digestive tract.Significance: This study has designed and developed a micro capsule robot in the digestive tract, which can achieve safe and efficient liquid sampling operations. The proposed robot can benefit the clinical diagnosis of digestive diseases, especially in the small intestine.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604641","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