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Mediterranean Electrotechnical Conference (MELECON)

作者： Carlos Rodrigues Miguel Correia João Abrantes Marco Rodrigues Jurandir Nadal C-BER - Centre for Biomedical Engineering Research INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal MovLab - Laboratory of Technologies for Interactions and Interfaces CICANT - Centre for Research in Applied Communication Culture and New Technologies Lisbon Portugal Department of Electronic and Systems Federal University of Pernambuco Recife Brazil Biomedical Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

This study presents variability assessment of real time measurements from in-vivo internal joint loads with instrumented implant during post-operative (PO) recovery process from total hip arthroplasty on daily living gait activities. A total of 112 trials walking supported by crutches in both hands, contralateral and ipsilateral sides, walking on treadmill at constant velocities, accelerating, decelerating and free walking, were assessed from 9 different patients ranging 0.3 to 76-month PO. Variability was assessed based on standard deviation of the vertical joint load normalized to each subject body weight with this metric adequacy to monitor PO recover.

关键词： Legged locomotion Temperature measurement Force measurement Instruments Force Time series analysis Implants

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Real-time in situ study of adsorption kinetics in PMMA thin films using infrared spectroscopy

Real-time in situ study of adsorption kinetics in PMMA thin ...

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Computational Optical Sensing and Imaging, COSI 2022

作者： Lee, Tse-Ang Kowal, Gina Peng, Jessie Hutter, Tanya Department of Mechanical Engineering University of Texas at Austin AustinTX78712 United States Department of Psychology University of Texas at Austin AustinTX78712 United States Department of Biomedical Engineering University of Texas at Austin AustinTX78712 United States Materials Science and Engineering Program Texas Materials Institute University of Texas at Austin AustinTX78712 United States

Studying adsorption in polymers is important for many applications. We present results on real-time in situ adsorption of ethanol and water in thin polymethyl methacrylate (PMMA) layers using attenuated total reflecti... 详细信息

ISBN: (纸本)9781557528209

关键词： Polymethyl methacrylates

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Super-Fast and Accurate Nonlinear Foot Deformation Prediction Using Graph Neural Networks

SSRN

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SSRN 2024年

作者： Kang, Taehyeon Kim, Jiho Lee, Hyobi Yum, Haeun Kwon, Chani Lee, Sangryun Lee, Taeyong Department of Mechanical and Biomedical Engineering Ewha Womans University Seoul03760 Korea Republic of Department of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Human Performance Lab Co. Ltd. #123a 150 Bugahyeon-ro Seodaemun-gu Seoul03759 Korea Republic of Ewha Womans University Korea Republic of

Recently, there has been a significant increase in the number of foot diseases, highlighting the importance of non-surgical treatments. Customized insoles, tailored to an individual's foot morphology, have emerged as a promising solution. However, the traditional design process of the customized insole is both slow and expensive due to the high computational complexity of finite element analysis (FEA) required to predict deformations of the foot. This study explores the applicability of a graph neural network (GNN) based on the MeshGraphNet framework to predict the 3-D shape of the foot under load and test the performance of GNN depending on the number of datasets. A total of 186 3-D undeformed foot CAD geometries are obtained from a series of 2-D foot images with deformations predicted through FEA. This FEA data is then used to train the GNN model, which aims to predict foot displacement with high accuracy and computation speed. After optimization of the weights of the GNN, the model remarkably outperformed FEA simulations in speed, being approximately 100 times faster, while maintaining high accuracy, with R2 values above 95% in predicting foot displacement. This breakthrough suggests that GNN models can greatly improve the efficiency and reduce the cost of manufacturing customized insoles, providing a significant advancement in non-surgical treatment options for foot conditions. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Complexity Analysis based on Parietal Fuzzy Entropy to Facilitate ADHD Diagnosis in Young Children 46

Complexity Analysis based on Parietal Fuzzy Entropy to Facil...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Huang, I-Wen Jheng, Yu-Ci Chen, I-Chun Ko, Li-Wei National Yang Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300040 Taiwan National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu300040 Taiwan National Yang Ming Chiao Tung University International Program in Interdisciplinary Neuroscience College of Biological Science and Technology Hsinchu300040 Taiwan Ton-Yen General Hospital Department of Physical Medicine and Rehabilitation Hsinchu302048 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Kaohsiung807378 Taiwan

ISBN: (纸本)9798350371499

Attention deficit hyperactivity disorder (ADHD) is the most common condition affecting the development of neurons in children. Therefore, early and accurate diagnosis of ADHD in young children is of paramount importance. In this study, the 8-channel wireless wearable EEG measurement device was employed to record EEG data from 30 children diagnosed with ADHD and 30 typical development (TD) young children aged 4-7 years. The data was collected both during rest and while the children performed a Kiddie Continuous Performance Test (K-CPT). We extract relative power spectral density (PSD) unaffected by factors like skull resistance and thickness. Additionally, a range of complex entropy values based on the time domain were extracted. These included sample entropy (SaEn), permutation entropy (PeEn), singular value decomposition entropy (SvdEn), and fuzzy entropy (FuEn). We compare the performance of k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and XGBoost, and utilized the sequential forward selection (SFS) feature selection method in the wrapper approach. Through this process, the study identified the most effective EEG data segments and feature subsets. The findings indicated that using a combination of resting and K-CPT EEG data yielded greater discriminability. Notably, the study found that extracting beta power from the right occipital lobe along with fuzzy entropy from the parietal lobe resulted in optimal accuracy of 90% in distinguishing between children with ADHD and TD children. These outcomes highlight the potential of relative PSD and complexity metrics to support the clinical diagnosis of early ADHD. Furthermore, these metrics may contain unique neurobiomarkers that could be valuable for devising early intervention strategies. © 2024 IEEE.

关键词： Diseases

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Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

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Computers, Materials & Continua 2021年第11期69卷 2031-2044页

作者： K.Muthumayil S.Manikandan S.Srinivasan JoséEscorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Biomedical Engineering Saveetha School of EngineeringSIMATSChennai602105India Electronic and Telecommunicacions Program Universidad Autónoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human *** spite of its prevalence and serious consequences,it is mostly diagnosed through manual *** risks of inappropriate,sub-standard and wrong or biased diagnosis are high in manual ***,there is a need exists for automatic diagnosis and classification method that can replace the manual *** is mainly classified into acute and chronic *** current research work proposed a computer-based application to classify the *** the feature extraction stage,we use excellent physical properties to improve the diagnostic system’s accuracy,based on Enhanced Color Co-Occurrence *** study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network(EVNN)*** proposed method achieved optimum accuracy in detection and classification of leukemia from WBC ***,the study results establish the superiority of the proposed method in automated diagnosis of *** values achieved by the proposed method in terms of sensitivity,specificity,accuracy,and error rate were 97.8%,89.9%,76.6%,and 2.2%,***,the system could predict the disease in prior through images,and the probabilities of disease detection are also highly optimistic.

关键词： White blood cells enhanced virtual neural networking segmentation feature extraction chronic lymphocytic leukemia

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Spectral Extremal Connectivity of Two-State Seizure Brain Waves

arXiv

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arXiv 2025年

作者： Talento, Mara Sherlin D. Richards, Jordan Pinto-Orellana, Marco Huser, Raphael Ombao, Hernando C. Statistics Program Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia Neuro-AI Laboratory Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia School of Mathematics Maxwell Institute for Mathematical Sciences University of Edinburgh EdinburghEH9 3FD United Kingdom Department of Biomedical Engineering University of California Irvine92697 United States

Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading findings when ignoring variations of connectivity in the tails of the distribution. This limitation becomes important when investigating extreme neural events that are characterized by large signal amplitudes. The focus of this paper is to examine connectivity in the tails of the distribution, as this reveals salient information that may be overlooked by standard methods. We develop a novel notion of spectral tail association of periodograms to study connectivity in the network of electroencephalogram (EEG) signals of seizure-prone neonates. We further develop a novel non-stationary extremal dependence model for multivariate time series that captures differences in extremal dependence during different brain phases, namely burst-suppression and non-burst-suppression. One advantage of our proposed approach is its ability to identify tail connectivity at key frequency bands that could be associated with outbursts of energy which may lead to seizures. We discuss these novel scientific findings alongside a comparison of the extremal behavior of brain signals for epileptic and non-epileptic patients. © 2025, CC BY.

关键词： Electroencephalography

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jen-Chien Yu Chun-Chieh Yang John Reuben Gilbert Rou-Jun Liu Yen-Jen Oyang Meng-Han Yang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan ROC Master Program in Statistics National Taiwan University Taipei Taiwan ROC Department of Computer Science & Information Engineering National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.

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Flexible Dual-Sided Polymer Microelectrode Array for Neurophysiological Recordings from an Insect Brain

Flexible Dual-Sided Polymer Microelectrode Array for Neuroph...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Xiang Liu Simon Sanchez Yan Gong Zebin Jiang Trevor Stevens Hasan Banan Debajit Saha Wen Li Department of Physiology Neuroscience Program Michigan State University East Lansing MI USA Institute for Quantitative Health Science and Engineering East Lansing MI USA Department of Biomedical Engineering Michigan State University East Lansing MI USA Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

Despite advances in modern microelectrode technologies, designing high-density flexible microelectrode arrays (MEAs) for small insect brains remains challenging, especially when aiming for dense microelectrode configurations on flexible substrates in restricted areas (e.g., 250×100 μm 2 ). To overcome these challenges, we developed a novel dual-sided design that doubles the channel count and density of MEAs within the same spatial footprint in comparison with the single-sided counterparts. A unique folding-annealing method is introduced to craft Parylene-based flexible dual-sided density MEAs. These MEAs, with overall dimensions of 20 μm in thickness and 120-230 μm in width, are specifically tailored for the locust antenna lobe, which is the central odor processing circuitry in insects. The efficacy of the dual-sided MEA for neurophysiological sensing of odors was validated in vivo using a neural circuitry (antennal lobe) of the locust brain.

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Predicting the Heterogeneous Chemo-Mechano-Biological Degeneration of Cartilage Using 3-D Biphasic Finite Elements

SSRN

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SSRN 2024年

作者： Rahman, Muhammed Masudur Watton, Paul N. Neu, Corey P. Pierce, David M. Department of Mechanical Engineering University of Connecticut Storrs CT United States Department of Computer Science Insigneo Institute for in silico Medicine University of Sheffield Sheffield United Kingdom Department of Mechanical Engineering and Materials Science University of Pittsburgh Pittsburgh United States Paul M. Rady Department of Mechanical Engineering University of Colorado BoulderCO United States Biomedical Engineering Program University of Colorado BoulderCO United States BioFrontiers Institute University of Colorado BoulderCO United States Department of Biomedical Engineering University of Connecticut Storrs CT United States

We establish a novel chemo-mechano-biological (CMB) modeling framework for cartilage implemented within 3-D nonlinear, finite element analyses. The framework integrates a nonlinear biphasic constitutive model with a signaling-pathways biochemical model to capture the coupled effects of mechanical stimuli, biochemical factors, and cellular activity on cartilage degeneration and remodeling. Through finite element simulations of cyclic compression, we explored cartilage degeneration similar to early-stage osteoarthritis (OA), demonstrating that oxygen-dependent, depth-specific metabolic activity yields more realistic patterns of degeneration compared to our previous models. Specifically, our results align with experimental observations, capturing depth-dependent cartilage degradation most pronounced in the superficial zone. Incorporating these insights, our framework provides a platform to investigate mechanisms of cartilage degeneration and simulate therapeutic interventions. We previously developed automated and publicly available tools to generate patient-specific knee models from MR Images, altogether enabling personalized diagnostics/prognostics and pre-/post-operative planning. Our CMB framework is also publicly available for academic use at https://***/imLab/FEVGnR-Plugin, offering a robust basis for advancing research and clinical strategies in understanding and managing osteoarthritis. © 2024, The Authors. All rights reserved.

关键词： Cartilage

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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