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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Edith Touchet-Valle Seelay Tasmim Taylor H. Ware Mary P. McDougall Department of Electrical and Computer Engineering Texas A&M University College Station TX USA Department of Biomedical Engineering Texas A&M University College Station TX USA Departments of Biomedical Engineering and Materials Science & Engineering Texas A&M University College Station TX USA Departments of Biomedical Engineering and Electrical and Computer Engineering Texas A&M University College Station TX USA

Implementation of multinuclear MRI/S as a diagnostic tool in clinical settings faces many challenges. One of those challenges is the development of highly sensitive multinuclear RF coils. Current multi-tuning techniques incorporate lossy components that impact the highest achievable SNR for at least one of the coil frequencies. As a result, optimization of multinuclear coil designs continues to be a priority for RF hardware engineers. To address this challenge, a new frequency switching technology that incorporates stimuli-responsive polymer materials was explored. Q measurements were used as a comparison metric between single-tuned, a standard switching network, and the proposed switching technology. The Q losses measured in the new switching method remained below 38% when compared to single-tuned coils. These results are consistent with low loss values reported using traditional switching networks. Furthermore, preliminary testing indicates that there is potential for improvement. These results establish the new technology as a promising alternative to traditional switching *** Relevance— A low loss multi-tuning technique for MRI radiofrequency coils has the potential of improving the study and diagnosis of disease.

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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Separation Sound Event Localization and Detection Using Neural Network and Time Frequency Masking

SSRN

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SSRN 2023年

作者： Ranny, Ranny Lestari, Dessi Puji Mengko, Tati Latifah Erawati Rajab School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics U-CoE AI-VL Institut Teknologi Bandung Bandung Indonesia Department of Biomedical Engineering School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Background: Sound processing frameworks have many kinds of approaches. The approaches are distinguished based on data and purpose of the sound processing research area. One of the research purposes are localization and detection of sound events. The research also uses non-overlapping data and overlapping data to test the sound processing algorithms. The previous research has a good accuracy with non-overlapping sound, but the accuracy decreased when it uses the overlapping data sound. Whereas the overlapping data sound appeared in a real condition. So, this research is developing a framework that can handle the non-overlapping and overlapping data. Objective: This research focused on overlapping sound processing. The using of overlapping sound in sound recognition system decreases the recognition accuracy. This research tries to improve the sound recognition system without eliminating the using of overlapping sound. Methods: This research uses a different approach than any other common research. This research used the non-overlapping data and separated sound data as the data train for sound event localization and detection (SELD). The separation of overlapping sound generates the separated sound and is used for detection the overlapping data sound. This new framework is called Separation ***: This Separation SELDnet framework gives 81% localization accuracy and 70% detection accuracy when the SELDnet only gives a lower accuracy for the overlapping ***: The Separation SELDnet gives a good opportunity to increase the sound recognition with using non overlapping and overlapping sound. This new method can be more adaptive with real condition. © 2023, The Authors. All rights reserved.

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Plasma-Treated Polymeric Biomaterials for Improved Surface and Cell Adhesion

arXiv

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arXiv 2025年

作者： Rondón, Jairo Gonzalez-Lizardo, Angel Biomedical & Chemical Engineering Departments Polytechnic University of Puerto Rico Puerto Rico San Juan United States Department of Electrical and Computer Engineering and Computer Science Polytechnic University of Puerto Rico Puerto Rico San Juan United States

Surface modification of polymeric biomaterials using plasma has emerged as an effective strategy to optimize the cell-material interface without compromising the structural properties of the material. This work presents a critical review of the impact of low-temperature plasma treatment on enhancing cell adhesion, with emphasis on the physicochemical changes induced on the surface of polymers commonly used in biomedical applications. The mechanisms of interaction between reactive plasma species and the polymer surface are analyzed, along with techniques used to introduce hydrophilic functional groups that improve wettability and biocompatibility. Scientific evidence demonstrates that this type of surface modification promotes greater cell spreading, anchorage, and proliferation, making it particularly useful in the design of tissue engineering scaffolds, implantable devices, and vascular prostheses. Finally, current and future trends in the development of smart plasma-functionalized biomaterials are discussed, highlighting their role in regenerative medicine. © 2025, CC BY-NC-ND.

关键词： Medical applications

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Multicluster Kernel Intuitionistic Fuzzy C-Means and State Transition Algorithm : Framework for Low Light Segmentation Imaging of Tuberculosis Bacilli Base of Semisupervised Approach

Multicluster Kernel Intuitionistic Fuzzy C-Means and State T...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Sari Ayu Wulandari I Ketut Eddy Purnama Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Biomedical Engineering Faculty of Electrical Engineering Universitas Dian Nuswantoro Semarang Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

Annotated images are required as ground truth for deep learning. Bacterial segmentation is requires a lot of time and effort when done manually. The autosegmentation task gets more challenging since bacterial images contain low light properties, which have an important effect on auto annotating tasks. In order to solve this issue, we present a system that includes a fuzzy-based clustering method that enhances bacterial object segmentation performance by utilizing the multicluster idea. The State Transform Algorithm (STA) is used to obtain starting centroids in order to increase stability, because the Kernel of Intuitionistic Fuzzy C- Means (KIFCM) is sensitive to starting centroids and hence sensitive to being stuck in local optima. The accuracy of KIFCM-STA with bicluster is poor in low-light images. To boost performance, the multicluster technique (MKIFCM-STA) is presented as a continuation hybrid of KIFCM-STA. This framework allows for the ideal amount of clusters (Silhouette) and cluster ranking to provide clusters containing bacterial objects (Topsis). In order to compare our method against four prior approaches (IFCM, KFCM, KIFCM, and KIFCM-STA), we compare its qualitative aspects (visualization of images) and quantitative aspects (average IOU, Dice, HD, ASD, and Accuracy). In low light image clustering tasks, our model significantly improves and achieves great results in terms of accuracy, with a score of 89.438%. This accomplishment highlights how crucial it is that our framework tackles the problem of low-light image clustering in images of bacteria, eventually improving the image auto-annotation procedure.

关键词： Deep learning Image segmentation Visualization Microorganisms Accuracy Clustering methods Clustering algorithms

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Online Transfer Learning for RSV Case Detection

Online Transfer Learning for RSV Case Detection

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IEEE International Conference on Healthcare informatics (ICHI)

作者： Yiming Sun Yuhe Gao Runxue Bao Gregory F. Cooper Jessi Espino Harry Hochheiser Marian G. Michaels John M. Aronis Chenxi Song Ye Ye Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh USA Department of Biomedical Informatics University of Pittsburgh Pittsburgh USA Department of Pediatrics University of Pittsburgh Pittsburgh USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various realworld applications. However, utilizing this technique for classification tasks with sequential data often faces challenges, primarily attributed to the scarcity of class labels. To address this challenge, we introduce Multi-Source Adaptive Weighting (MSAW), an online multi-source transfer learning method. MSAW integrates a dynamic weighting mechanism into an ensemble framework, enabling automatic adjustment of weights based on the relevance and contribution of each source (representing historical knowledge) and target model (learning from newly acquired data). We demonstrate the effectiveness of MSAW by applying it to detect Respiratory Syncytial Virus cases within Emergency Department visits, utilizing multiple years of electronic health records from the University of Pittsburgh Medical Center. Our method demonstrates performance improvements over many baselines, including refining pre-trained models with online learning as well as three static weighting approaches, showing MSAW's capacity to integrate historical knowledge with progressively accumulated new data. This study indicates the potential of online transfer learning in healthcare, particularly for developing machine learning models that dynamically adapt to evolving situations where new data is incrementally accumulated.

关键词： Adaptation models computer viruses Biological system modeling Transfer learning Refining Medical services Data models

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Comparative Analysis of Transfer Learning Models for Breast Cancer Classification

arXiv

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arXiv 2024年

作者： Eskandari, Sania Eslamian, Ali Cheng, Qiang Department of Electrical and Computer Engineering University of Kentucky Lexington United States Department of Computer Science University of Kentucky Lexington United States Institute for Biomedical Informatics University of Kentucky Lexington United States

The classification of histopathological images is crucial for the early and precise detection of breast cancer. This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a thorough comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet. This analysis was carried out using a large dataset of 277,524 image patches. Our research makes a substantial contribution to the field by offering a comprehensive assessment of the performance of each model. We particularly highlight the exceptional efficacy of attention-based mechanisms in the ViT model, which achieved a remarkable validation accuracy of 93%, surpassing conventional convolutional networks. This study highlights the promise of advanced machine learning approaches in clinical settings, offering improved precision as well as efficiency in breast cancer diagnosis. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Causal Effects of Environmental Exposures and Biological Traits on the Difference between Phenotypic and Chronological Ages

Causal Effects of Environmental Exposures and Biological Tra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Daniele Pala Yuezhi Xie Jia Xu Yuqin Zhang Li Shen Department of Computer Electrical and Biomedical Engineering University of Pavia Pavia Italy Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia USA

Aging is a physiological process associated with numerous cardiovascular, degenerative and neurological conditions. The current demographic changes in the world, characterized by an increasing average age especially in Europe and North America, are causing an increment in the prevalence of such conditions, leading to a public health problem as more resources are required to manage treatments. Recent research has defined a new score named Phenotypic Age, i.e. an adjusted age that takes into account the current health status considering a series of biomarkers, that can be useful to provide a more precise estimation of the probability of developing aging-related conditions. Prevention of such conditions can be performed more efficiently by studying the mechanisms that lead to an increased phenotypic age rather than attempting to treat a patient that has already a high health risk. In this work, we combine pairwise association techniques and mediation analysis to define a strategy to investigate the inner causal mechanisms that lead from specific environmental exposures to an increasing gap between phenotypic and chronological age, considering the influence of biological variables. Four environmental exposures and 11 biological traits have been identified in the NHANES dataset, and each trait has been tested as a mediation variable for each exposure. Almost all mediations reported significant indirect effects with specific results that provide new insights into the causal mechanisms that lead to a deranged aging rate.

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Wrist EMG Improves Gesture Classification for Stroke Patients

Wrist EMG Improves Gesture Classification for Stroke Patient...

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IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： Connor D. Olsen W. Caden Hamrick Samuel R. Lewis Marta M. Iverson Jacob A. George The Department of Electrical and Computer Engineering University of Utah Salt Lake City UT USA Department of Electrical and Computer Engineering Mercer University Macon GA USA The Department of Biomedical Engineering University of Utah Salt Lake City UT USA The Departments of Electrical and Computer Engineering Physical Medicine and Rehabilitation Biomedical Engineering and Mechanical Engineering University of Utah Salt Lake City UT USA

Electromyography (EMG) is a popular human-machine interface for hand gesture control of assistive and rehabilitative technology. EMG can be used to estimate motor intent even when an individual cannot physically move due to weakness or paralysis. EMG is traditionally recorded from the extrinsic hand muscles located in the forearm. However, the wrist has become an increasingly attractive recording location for commercial applications as EMG sensors can be integrated into wrist-worn wearables (e.g., watches, bracelets). Here we explored the impact that recording EMG from the wrist, instead of the forearm, has on stroke patients with upper-limb hemiparesis. We show that EMG signal-to-noise ratio is significantly worse at the paretic wrist relative to the paretic forearm and non-paretic wrist. Despite this, we also show that the ability to classify hand gestures from EMG was significantly better at the paretic wrist relative to the paretic forearm. Our results also provide guidance as to the ideal gestures for each recording location. Namely, single-digit gestures appeared easiest to classify from both forearm and wrist EMG on the paretic side. These results suggest commercialization of wrist-worn EMG would benefit stroke patients by providing more accurate EMG control in a more widely adopted wearable formfactor.

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Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning

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computers in Biology and Medicine 2025年 192卷 110150-110150页

作者： Iyer, Balaji Deoghare, Smruti Ranjan, Krish Aronow, Bruce J. Prasath, V.B. Surya Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States Department of Electrical Engineering and Computer Science University of Cincinnati OH45221 United States

Indirect Immunofluorescence (IIF) stained Human Epithelial (HEp-2) cells are considered the gold standard for detecting autoimmune diseases. Accurate cell segmentation, though often viewed as an intermediary step to downstream tasks like classification, significantly enhances overall performance when executed with precision. In this study, we conduct a systematic literature review of HEp-2 cell segmentation techniques, identifying 28 key papers utilizing traditional image processing, machine learning classifiers, deep convolutional neural networks (CNNs), and generative adversarial network (GAN) frameworks. Building on these insights, we benchmark 17 CNN models without pretraining and 8 CNN models pretrained on ImageNet using both Frozen Encoder and Tunable Encoder strategies on the I3A dataset. Cross-validation (CV) and Benjamini–Hochberg (BH) significance correction were employed to ensure statistical rigor in model comparisons. Domain-Specific Pretraining (DSPT) experiments demonstrated performance improvements, particularly for underrepresented classes, while Data Augmentation strategies (DA-1 and DA-2) revealed distinct impacts across model categories. GAN-based segmentation experiments using the top-performing CNN architectures as generators within a Pix2Pix framework revealed performance degradation due to data limitations and adversarial training instabilities. Nonetheless, GANs displayed class-specific improvements in visual alignment of segmentation masks. Results were evaluated comprehensively across eight performance metrics, including Dice, IOU, Accuracy, Precision, Sensitivity, Specificity, AU-ROC and AU-PR. This work offers a robust benchmarking of state-of-the-art CNN, GAN, and Transformer-based models for HEp-2 cell segmentation, providing valuable insights for future research directions, including ensemble approaches, dynamic patch sampling, and diffusion models. © 2025 The Authors

关键词： Generative adversarial networks

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