IEEE Journal of Biomedical and Health Informatics最新文献

Machine Learning Identification and Classification of Mitosis and Migration of Cancer Cells in a Lab-on-CMOS Capacitance Sensing platform. 在实验室-CMOS 电容传感平台上对癌细胞的有丝分裂和迁移进行机器学习识别和分类。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-12 DOI: 10.1109/JBHI.2024.3486251

Ching-Yi Lin, Marc Dandin

{"title":"Machine Learning Identification and Classification of Mitosis and Migration of Cancer Cells in a Lab-on-CMOS Capacitance Sensing platform.","authors":"Ching-Yi Lin, Marc Dandin","doi":"10.1109/JBHI.2024.3486251","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3486251","url":null,"abstract":"Cell culture assays play a vital role in various fields of biology. Conventional assay techniques like immunohistochemistry, immunofluorescence, and flow cytometry offer valuable insights into cell phenotype and behavior. However, each of these techniques requires labeling or staining, and this is a major drawback, specifically in applications that require compact and integrated analytical devices. To address this shortcoming, CMOS capacitance sensors capable of conducting label-free cell culture assays have been proposed. In this paper, we present a computational framework for further augmenting the capabilities of these capacitance sensors. In our framework, identification and classification of mitosis and migration are achieved by leveraging observations from measured capacitance time series data. Specifically, we engineered two time series features that enable discriminating cell behaviors at the single-cell level. Our feature representation achieves an area under curve (AUC) of 0.719 in the receiver operating characteristic (ROC) curve. Additionally, we show that our feature representation technique is applicable across arbitrary experiments, as validated by a leave-one-run-out test yielding an F-1 score of 0.803 and a G-Mean of 0.647.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619319","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

Multi-level Noise Sampling from Single Image for Low-dose Tomography Reconstruction. 从单幅图像中提取多级噪声样本，用于低剂量断层扫描重建

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3486726

Weiwen Wu, Yifei Long, Zhifan Gao, Guang Yang, Fangxiao Cheng, Jianjia Zhang

{"title":"Multi-level Noise Sampling from Single Image for Low-dose Tomography Reconstruction.","authors":"Weiwen Wu, Yifei Long, Zhifan Gao, Guang Yang, Fangxiao Cheng, Jianjia Zhang","doi":"10.1109/JBHI.2024.3486726","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3486726","url":null,"abstract":"Low-dose digital radiography (DR) and computed tomography (CT) become increasingly popular due to reduced radiation dose. However, they often result in degraded images with lower signal-to-noise ratios, creating an urgent need for effective denoising techniques. The recent advancement of the single-image-based denoising approach provides a promising solution without requirement of pairwise training data, which are scarce in medical imaging. These methods typically rely on sampling image pairs from a noisy image for inter-supervised denoising. Although enjoying simplicity, the generated image pairs are at the same noise level and only include partial information about the input images. This study argues that generating image pairs at different noise levels while fully using the information of the input image is preferable since it could provide richer multi-perspective clues to guide the denoising process. To this end, we present a novel Multi-Level Noise Sampling (MNS) method for low-dose tomography denoising. Specifically, MNS method generates multi-level noisy sub-images by partitioning the highdimensional input space into multiple low-dimensional subspaces with a simple yet effective strategy. The superiority of the MNS method in single-image-based denoising over the competing methods has been investigated and verified theoretically. Moreover, to bridge the gap between selfsupervised and supervised denoising networks, we introduce an optimization function that leverages prior knowledge of multi-level noisy sub-images to guide the training process. Through extensive quantitative and qualitative experiments conducted on large-scale clinical low-dose CT and DR datasets, we validate the effectiveness and superiority of our MNS approach over other state-of-the-art supervised and self-supervised methods.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619323","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

LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI. LGG-NeXt：利用二维结构磁共振成像诊断阿尔茨海默病的下一代 CNN 和变压器混合模型。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3495835

Jing Bai, Zhengyang Zhang, Yue Yin, Weikang Jin, Talal Ahmed Ali Ali, Yong Xiong, Zhu Xiao

{"title":"LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI.","authors":"Jing Bai, Zhengyang Zhang, Yue Yin, Weikang Jin, Talal Ahmed Ali Ali, Yong Xiong, Zhu Xiao","doi":"10.1109/JBHI.2024.3495835","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3495835","url":null,"abstract":"Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. However, the existing methods have shortcomings, such as inability to learn local and global information and the inability to extract effective features. In this paper, we propose a lightweight classification network Local and Global Graph ConvNeXt. This model has a hybrid architecture of convolutional neural network and Transformers. We build the Global NeXt Block and the Local NeXt Block to extract the local and global features of the structural magnetic resonance imaging (sMRI). These two blocks are optimized by adding global multilayer perceptron and locally grouped attention, respectively. Then, the features are fed into the pixel graph neural network to aggregate the valid pixel features using mask attention. In addition, we decoupled the loss by category to optimize the calculation of the loss. This method was tested on slices of the processed sMRI datasets from ADNI and achieved excellent performance. Our model achieves 95.81% accuracy with fewer parameters and floating point operations per second (FLOPS) than other classical efficient models in the diagnosis of AD.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619317","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

EEG Detection and Prediction of Freezing of Gait in Parkinson's Disease Based on Spatiotemporal Coherent Modes. 基于时空相干模式的帕金森病步态冻结脑电图检测与预测

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496074

Jun Li, Yuzhu Guo

{"title":"EEG Detection and Prediction of Freezing of Gait in Parkinson's Disease Based on Spatiotemporal Coherent Modes.","authors":"Jun Li, Yuzhu Guo","doi":"10.1109/JBHI.2024.3496074","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496074","url":null,"abstract":"Objective: Freezing of gait (FOG) in Parkinson's disease has a complex neurological mechanism. Compared with other modalities, electroencephalogram (EEG) can reflect FOG-related brain activity of both motor and non-motor symptoms. However, EEG-based FOG prediction methods often extract time, spatial, frequency, time-frequency, or phase information separately, which fragments the coupling among these heterogeneous features and cannot completely characterize the brain dynamics when FOG occurs.Methods: In this study, dynamic spatiotemporal coherent modes of EEG were studied and used for FOG detection and prediction. A dynamic mode decomposition (DMD) method was first applied to extract the spatiotemporal coherent modes. Dynamic changes of the spatiotemporal modes, in both amplitude and phase of motor-related frequency bands, were evaluated with analytic common spatial patterns (ACSP) to extract the essential differences among normal, freezing, and transitional gaits.Results: The proposed method was verified in practical clinical data. Results showed that, in the detection task, the DMD-ACSP achieved an accuracy of 86.4 ± 3.6% and a sensitivity of 83.5 ± 4.3%. In the prediction task, 86.5 ± 3.2% accuracy and 86.7 ± 7.8% sensitivity were achieved.Conclusion: Comparative studies showed that the DMD-ACSP method significantly improves FOG detection and prediction performance. Moreover, the DMD-ACSP reveals the spatial patterns of dynamic brain functional connectivity, which best discriminate the different gaits.Significance: The spatiotemporal coherent modes may provide a useful indication for personalized intervention and transcranial magnetic stimulation neuromodulation in medical practices.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619301","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

Self-supervised Multi-scale Multi-modal Graph Pool Transformer for Sellar Region Tumor Diagnosis. 用于塞拉区域肿瘤诊断的自监督多尺度多模态图池变换器

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496700

Baiying Lei, Gege Cai, Yun Zhu, Tianfu Wang, Lei Dong, Cheng Zhao, Xinzhi Hu, Huijun Zhu, Lin Lu, Feng Feng, Ming Feng, Renzhi Wang

{"title":"Self-supervised Multi-scale Multi-modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.","authors":"Baiying Lei, Gege Cai, Yun Zhu, Tianfu Wang, Lei Dong, Cheng Zhao, Xinzhi Hu, Huijun Zhu, Lin Lu, Feng Feng, Ming Feng, Renzhi Wang","doi":"10.1109/JBHI.2024.3496700","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496700","url":null,"abstract":"The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of pa-tients. Magnetic resonance imaging (MRI) has proven to be an effective tool for the early detection of sellar region tumors. However, the existing sellar region tumor diagnosis still remains challenging due to the small amount of dataset and data imbalance. To overcome these challenges, we propose a novel self-supervised multi-scale multi-modal graph pool Transformer (MMGPT) network that can enhance the multi-modal fusion of small and imbalanced MRI data of sellar region tumors. MMGPT can strengthen feature interaction between multi-modal images, which makes our model more robust. A contrastive learning equipped auto-encoder (CAE) via self-supervised learning (SSL) is adopted to learn more detailed information between different samples. The proposed CAE transfers the pre-trained knowledge to the downstream tasks. Finally, a hybrid loss is equipped to relieve the performance degradation caused by data imbalance. The experimental results show that the proposed method outperforms state-of-the-art methods and obtains higher accuracy and AUC in the classification of sellar region tumors.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619326","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

BloodPatrol: Revolutionizing Blood Cancer Diagnosis - Advanced Real-Time Detection Leveraging Deep Learning & Cloud Technologies. BloodPatrol：彻底改变血癌诊断--利用深度学习和云技术进行先进的实时检测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496294

Jinhang Wei, Longyue Wang, Zhecheng Zhou, Linlin Zhuo, Xiangxiang Zeng, Xiangzheng Fu, Quan Zou, Keqin Li, Zhongjun Zhou

{"title":"BloodPatrol: Revolutionizing Blood Cancer Diagnosis - Advanced Real-Time Detection Leveraging Deep Learning & Cloud Technologies.","authors":"Jinhang Wei, Longyue Wang, Zhecheng Zhou, Linlin Zhuo, Xiangxiang Zeng, Xiangzheng Fu, Quan Zou, Keqin Li, Zhongjun Zhou","doi":"10.1109/JBHI.2024.3496294","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496294","url":null,"abstract":"Cloud computing and Internet of Things (IoT) technologies are gradually becoming the technological changemakers in cancer diagnosis. Blood cancer is an aggressive disease affecting the blood, bone marrow, and lymphatic system, and its early detection is crucial for subsequent treatment. Flow cytometry has been widely studied as a commonly used method for detecting blood cancer. However, the high computation and resource consumption severely limit its practical application, especifically in regions with limited medical and computational resources. In this study, with the help of cloud computing and IoT technologies, we develop a novel blood cancer dynamic monitoring diagnostic model named BloodPatrol based on an intelligent feature weight fusion mechanism. The proposed model is capable of capturing the dual-view importance relationship between cell samples and features, greatly improving prediction accuracy and significantly surpassing previous models. Besides, benefiting from the powerful processing ability of cloud computing, BloodPatrol can run on a distributed network to efficiently process large-scale cell data, which provides immediate and scalable blood cancer diagnostic services. We have also created a cloud diagnostic platform to facilitate access to our work, the latest access link and updates are available at: https://github.com/kkkayle/BloodPatrol.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619299","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

SleepECG-Net: explainable deep learning approach with ECG for pediatric sleep apnea diagnosis. SleepECG-Net：利用心电图诊断小儿睡眠呼吸暂停的可解释深度学习方法。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3495975

Clara Garcia-Vicente, Gonzalo C Gutierrez-Tobal, Fernando Vaquerizo-Villar, Adrian Martin-Montero, David Gozal, Roberto Hornero

{"title":"SleepECG-Net: explainable deep learning approach with ECG for pediatric sleep apnea diagnosis.","authors":"Clara Garcia-Vicente, Gonzalo C Gutierrez-Tobal, Fernando Vaquerizo-Villar, Adrian Martin-Montero, David Gozal, Roberto Hornero","doi":"10.1109/JBHI.2024.3495975","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3495975","url":null,"abstract":"Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the standard diagnostic approach, faces challenges in accessibility and complexity, leading to underdiagnosis. To simplify OSA diagnosis, deep learning (DL) algorithms have been developed using cardiac signals, but they often lack interpretability. Our study introduces a novel interpretable DL approach (SleepECG-Net) for directly estimating OSA severity in at-risk children. A combination of convolutional and recurrent neural networks (CNN-RNN) was trained on overnight electrocardiogram (ECG) signals. Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable Artificial Intelligence (XAI) algorithm, was applied to explain model decisions and extract ECG patterns relevant to pediatric OSA. Accordingly, ECG signals from the semi-public Childhood Adenotonsillectomy Trial (CHAT, n = 1610) and Cleveland Family Study (CFS, n = 64), and the private University of Chicago (UofC, n = 981) databases were used. OSA diagnostic performance reached 4-class Cohen's Kappa of 0.410, 0.335, and 0.249 in CHAT, UofC, and CFS, respectively. The proposal demonstrated improved performance with increased severity along with heightened cardiovascular risk. XAI findings highlighted the detection of established ECG features linked to OSA, such as bradycardia-tachycardia events and delayed ECG patterns during apnea/hypopnea occurrences, focusing on clusters of events. Furthermore, Grad-CAM heatmaps identified potential ECG patterns indicating cardiovascular risk, such as P, T, and U waves, QT intervals, and QRS complex variations. Hence, SleepECG-Net approach may improve pediatric OSA diagnosis by also offering cardiac risk factor information, thereby increasing clinician confidence in automated systems, and promoting their effective adoption in clinical practice.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619328","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

Biomedical Information Integration via Adaptive Large Language Model Construction. 通过自适应大型语言模型构建实现生物医学信息整合。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496495

Xingsi Xue, Mu-En Wu, Fazlullah Khan

{"title":"Biomedical Information Integration via Adaptive Large Language Model Construction.","authors":"Xingsi Xue, Mu-En Wu, Fazlullah Khan","doi":"10.1109/JBHI.2024.3496495","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496495","url":null,"abstract":"Integrating diverse biomedical knowledge information is essential to enhance the accuracy and efficiency of medical diagnoses, facilitate personalized treatment plans, and ultimately improve patient outcomes. However, Biomedical Information Integration (BII) faces significant challenges due to variations in terminology and the complex structure of entity descriptions across different datasets. A critical step in BII is biomedical entity alignment, which involves accurately identifying and matching equivalent entities across diverse datasets to ensure seamless data integration. In recent years, Large Language Model (LLMs), such as Bidirectional Encoder Representations from Transformers (BERTs), have emerged as valuable tools for discerning heterogeneous biomedical data due to their deep contextual embeddings and bidirectionality. However, different LLMs capture various nuances and complexity levels within the biomedical data, and none of them can ensure their effectiveness in all heterogeneous entity matching tasks. To address this issue, we propose a novel Two-Stage LLM construction (TSLLM) framework to adaptively select and combine LLMs for Biomedical Information Integration (BII). First, a Multi-Objective Genetic Programming (MOGP) algorithm is proposed for generating versatile high-level LLMs, and then, a Single-Objective Genetic Algorithm (SOGA) employs a confidence-based strategy is presented to combine the built LLMs, which can further improve the discriminative power of distinguishing heterogeneous entities. The experiment utilizes OAEI's entity matching datasets, i.e., Benchmark and Conference, along with LargeBio, Disease and Phenotype datasets to test the performance of TSLLM. The experimental findings validate the efficiency of TSLLM in adaptively differentiating heterogeneous biomedical entities, which significantly outperforms the leading entity matching techniques.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619297","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

Functional Data Analysis of Hand Rotation for Open Surgical Suturing Skill Assessment. 用于开放手术缝合技能评估的手部旋转功能数据分析。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496122

Amir Mehdi Shayan, David B Hitchcock, Simar Singh, Jianxin Gao, Richard E Groff, Ravikiran B Singapogu

{"title":"Functional Data Analysis of Hand Rotation for Open Surgical Suturing Skill Assessment.","authors":"Amir Mehdi Shayan, David B Hitchcock, Simar Singh, Jianxin Gao, Richard E Groff, Ravikiran B Singapogu","doi":"10.1109/JBHI.2024.3496122","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496122","url":null,"abstract":"This study explores the application of functional data analysis (FDA) to hand roll velocity during radial suturing on the SutureCoach bench simulator for evaluating open suturing performance. By treating temporal sensor data as mathematical functions, FDA provides a holistic view of the dynamic changes in hand roll, offering comprehensive assessments that are easily interpretable and clinically relevant. Cluster analysis was performed on hand roll profiles from 96 subjects, categorized into advanced surgeons, trainee surgeons, and novices. Functional k-means, using dynamic time-warping to align curves, were used to partition the data into two preset numbers of clusters (3 and 6). Both clustering models (3-cluster and 6-cluster) effectively clustered performance into groups with distinct characteristics and levels of skill (evident from visual inspection of cluster centroids). The relationship between cluster membership and suturing skills was corroborated using proxy measures of skill: expert global rating scale ratings, clinical status and expertise, and simulator-derived metrics. The findings of this study offer valuable insight into essential components of suturing skill and can improve the autonomy and efficiency of simulation-based suturing training. The clinical relevance of our results is immediately pertinent to the field of surgical skill assessment, where FDA-based methods could potentially be employed for objective feedback and training.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619303","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

Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks. 利用黎曼几何网络对运动意象脑电图进行多级分类的框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-11 DOI: 10.1109/JBHI.2024.3496757

Yuxuan Shi, Aimin Jiang, Ju Zhong, Min Li, Yanping Zhu

{"title":"Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks.","authors":"Yuxuan Shi, Aimin Jiang, Ju Zhong, Min Li, Yanping Zhu","doi":"10.1109/JBHI.2024.3496757","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3496757","url":null,"abstract":"In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM) of electroencephalogram (EEG) signals plays a critical role in accurate classification. Given that SCMs are symmetric positive definite (SPD), Riemannian geometry is widely utilized to extract classification features. However, calculating distances between SCMs is computationally intensive due to operations like eigenvalue decomposition, and classical optimization techniques, such as gradient descent, cannot be directly applied to Riemannian manifolds, making the computation of the Riemannian mean more complex and reliant on iterative methods or approximations. In this paper, we propose a novel multiclass classification framework that integrates Riemannian geometry and neural networks to mitigate these challenges. The framework comprises two modules: a Riemannian module with multiple branches and a classification module. During training, a fusion loss function is introduced to update the branch corresponding to the true label, while other branches are updated using different loss functions along with the classification module. Comprehensive experiments on four sets of MI EEG data demonstrate the efficiency and effectiveness of the proposed model.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619321","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