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Novel method for modeling 2d and 3D shapes analytically with biomedical applications

Multimedia Tools and Applications 2025年 1-43页

作者： Abu-Qasmieh, Isam Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid Jordan

A novel, fast and simple approach to model full-analytical two-dimensional (2D) and three-dimensional (3D) spatial shapes is presented. The concept of this study is inspired from the behavior of the Butterworth Low-pass filter, used in signal and image processing, at different orders, where it behaves like ideal filter as the filter’s order goes to infinity and close to Gaussian (normal) distribution for very small orders. Different 2D polygons are modeled such as Rectangle, Parallelogram, Trapezoid, Triangle, Rhombus-Diamond, Hexagon and Octagon. Also, different 3D polygons are modeled such as Cuboid, Parallelepiped, Cylinder, Prism, Pyramid, Cone, Hexagonal-Prism, Octagon-Prism, Dough-Rolling-Pin and others. Those 2D and 3D shapes are modeled by contributing the Butterworth filter formula in the semi-axes of the ellipse and ellipsoid mathematical equations respectively. The 2D and 3D polygons can be modeled with sharp or smooth edges by changing the Butterworth order. By modifying some of the 3D shapes mathematical formula other biomedical 3D shapes can be modeled such as Bacillus-Bacteria, Bacteriophage and Sickle red blood cell 3D shapes. The 2D and 3D shapes can be selectively shifted or/and rotated with different angles using the 2D and 3D rotation matrix respectively without changing the relative Euclidean distances between the shape’s pixels. The 2D or 3D generated shapes can be detected using a powerful tool, for graphic element extraction from images, such as Hough transform for recognition of the scale, center coordinates and rotation angles for specific parametric shape in 2D, or 3D space respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Butterworth filters

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Quantitative Analysis of Side Effect Severity using the D-SESS Scale: An Observational Study with Analytical Techniques 24

Quantitative Analysis of Side Effect Severity using the D-SE...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Gaddam, Vijay Kumar Veldandi, Amulya Godana, Akhila Siri Sripathi, Surya Rao Gudisa, Timothy Department of BioMedical Engineering and Informatics Indiana University IndianapolisIN United States

ISBN: (纸本)9798400710162

The study presented in this paper introduces the Drug Side Effect Severity Scale (D-SESS), a novel quantitative metric designed to assess the severity of drug side effects. By applying natural language processing (NLP) techniques like tokenization for preprocessing and developing clustering and predictive models using machine learning algorithms, this research aims to provide a more nuanced understanding of drug side effects. This categorizes side effects into mild, moderate, and severe based on their impact on patient health. This paper details the methodology behind creating the D-SESS, its initial validation, and the application of clustering models such as K-Means, and Agglomerative Clustering to organize drugs based on their side effect profiles. Predictive modeling further enhances the utility of D-SESS by enabling the assignment of drugs to clusters, facilitating a comprehensive analysis of drug safety. The Drug Side Effect Severity Scale (D-SESS) aims to address this gap by introducing a quantitative metric designed to offer a more nuanced understanding of drug side effects. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Cuffless Blood Pressure Estimation Based on PPG Features 6th

Cuffless Blood Pressure Estimation Based on PPG Features

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6th International Conference on biomedical and Health informatics, ICBHI 2024

作者： Chen, Yu-Xian Sun, Ming-Hsuan Lu, Chi-Jen Hung, Wen-Kai Lin, Yue-Der Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung40724 Taiwan

ISBN: (纸本)9783031863226

Cardiovascular diseases (CVD) are a leading cause of death, encompassing conditions like heart disease, hypertension, and diabetes, which increase the risk of myocardial infarction and stroke. Continuous monitoring of blood pressure is crucial due to its significant correlation with these conditions. However, traditional methods are often invasive, causing discomfort and potential infections. This study aims to develop a continuous, cuffless blood pressure detection system using data from approximately 9,000 patients in the MIMIC II database, totaling over 2 million records. The system utilizes photoplethysmography (PPG) to extract temporal and waveform features, with regression analysis for feature reduction. A Transformer-based deep learning model is employed to establish the relationship between PPG features and blood pressure. The mean error (ME) and standard deviation (SD) for systolic (SBP) and diastolic blood pressure (DBP) are −0.14 ± 9.05 and −1.15 ± 5.45, respectively. According to AAMI and BHS standards, diastolic pressure meets AAMI criteria, with systolic and diastolic pressures achieving Grade B and Grade a levels, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photoplethysmography

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Enhancing Hand Movement Recognition: A Hybrid Fuzzy Deep Neural Network Approach with Time-Frequency EMG Representations 5

Enhancing Hand Movement Recognition: A Hybrid Fuzzy Deep Neu...

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5th National and the 1st International Conference on Applied Research in Electrical engineering, AREE 2025

作者： Bijarbaneh, Peyman Afshari Noori, Fatemeh Faramarzi, Shima Mousavi, Seyed Amirhossein Department of Informatics Genoa Italy Azad University of Mashhad Department of Biomedical Engineering Mashhad Iran

ISBN: (纸本)9798350377170

This study proposes a novel Hybrid Fuzzy Deep Neural Network (HFDNN) for recognizing complex hand movements using electromyography (EMG) signals. The system integrates fuzzy logic for managing uncertainty with deep neural networks (DNNs) for learning spatial and temporal features of EMG signals. Six hand movements, including lifting heavy and light objects, grasping pen-like and card-like objects, making a fist, and opening the hand, were analyzed. EMG signals were preprocessed using a band-pass filter and normalized, followed by feature extraction through short-time Fourier transform (STFT) to generate time-frequency representations as input to the HFDNN. The proposed model achieved a classification accuracy of 96.8%, with precision, recall, and F1-scores averaging 95.7%, 97.2%, and 96.3%, respectively. Compared to traditional CNN, RNN, and hybrid CNN-RNN models, the HFDNN demonstrated superior performance, validated through statistical significance testing (p > 0.01). The inclusion of fuzzy rules enhanced decision-making, particularly for overlapping classes. This work underscores the efficacy of hybrid approaches in capturing the intricacies of hand movement signals, paving the way for advancements in prosthetics control and rehabilitation. Comparative analysis with recent studies highlights the proposed methods robustness and potential for real-world applications. © 2025 IEEE.

关键词： Electromyography

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Personalizing Federated Instrument Segmentation With Visual Trait Priors in Robotic Surgery

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IEEE Transactions on biomedical engineering 2025年第6期72卷 1886-1896页

作者： Xu, Jialang Wang, Jiacheng Yu, Lequan Stoyanov, Danail Jin, Yueming Mazomenos, Evangelos B. University College London UCL Hawkes Institute The Department of Medical Physics and Biomedical Engineering London United Kingdom Xiamen University Department of Computer Science School of Informatics Xiamen China The University of Hong Kong Department of Statistics and Actuarial Science Hong Kong National University of Singapore Department of Biomedical Engineering The Department of Electrical and Computer Engineering Singapore

Personalized federated learning (PFL) for surgical instrument segmentation (SIS) is a promising approach. It enables multiple clinical sites to collaboratively train a series of models in privacy, with each model tailored to the individual distribution of each site. Existing PFL methods rarely consider the personalization of multi-headed self-attention, and do not account for appearance diversity and instrument shape similarity, both inherent in surgical scenes. We thus propose PFedSIS, a novel PFL method with visual trait priors for SIS, incorporating global-personalized disentanglement (GPD), appearance-regulation personalized enhancement (APE), and shape-similarity global enhancement (SGE), to boost SIS performance in each site. GPD represents the first attempt at head- wise assignment for multi-headed self-attention personalization. To preserve the unique appearance representation of each site and gradually leverage the inter-site difference, APE introduces appearance regulation and provides customized layer- wise aggregation solutions via hypernetworks for each site's personalized parameters. The mutual shape information of instruments is maintained and shared via SGE, which enhances the cross-style shape consistency on the image level and computes the shape-similarity contribution of each site on the prediction level for updating the global parameters. PFedSIS outperforms state-of-the-art methods with +1.51% Dice, +2.11% IoU, -2.79 ASSD, -15.55 HD95 performance gains. © 1964-2012 IEEE.

关键词： Robotic surgery

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Mechanoadaptive Bioelectronics for Deep Tissue Sensing

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Chinese Journal of Chemistry 2025年第5期43卷 567-584页

作者： Xueyang Ren Yuehui Yuan Jianqing Li Benhui Hu School of Biological Science and Medical Engineering Southeast UniversityNanjingJiangsu210096 China School of Biomedical Engineering and Informatics Nanjing Medical UniversityNanjingJiangsu211166 China School of Instrument Science and Engineering Southeast UniversityNanjingJiangsu210096 China

Comprehensive Summary Deep-tissue physiological signals are critical for accurate disease *** clinical equipment,however,often falls short of enabling continuous,long-term *** and implantable flexible electronics offer a promising avenue for addressing this limitation,allowing in vivo signal collection and paving the way for early diagnosis and personalized treatment.A major challenge lies in ensuring that these devices seamlessly integrate with the diverse physiological microenvironments throughout the human body.

关键词： Wearable electronics Implantable electronics Deep-tissue sensing Mechanoadaptive electronics Shape adaptive electronics Flexible electronics Stimuli-responsivepolymers Hydrogels

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Continuous Blood Pressure Estimation Using PPG Signals Without Cuff 6th

Continuous Blood Pressure Estimation Using PPG Signals Witho...

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6th International Conference on biomedical and Health informatics, ICBHI 2024

作者： Hung, Wen-Kai Sun, Ming-Hsuan Lu, Chi-Jen Chen, Yu-Xian Lin, Yue-Der Master’s Program in the Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung40724 Taiwan

ISBN: (纸本)9783031863226

Cardiovascular diseases (CVD) are one of the major global health threats, causing millions of deaths each year. According to data from the World Health Organization, over 500 million people are affected by cardiovascular diseases, resulting in over 20 million deaths in 2021 alone, accounting for about one-third of the total global deaths. Invasive blood pressure measurement methods not only cause discomfort to patients but also pose a risk of infection. Meanwhile, cuffless cuff-based methods cannot provide continuous monitoring. Therefore, the purpose of this study is to develop a cuffless continuous blood pressure measurement system based on photoplethysmography (PPG). By using waveform measurement technology, we aim to provide a more comfortable and safer method for blood pressure monitoring, thereby enhancing the management of cardiovascular diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blood pressure

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Structural characterization and immunomodulatory activities of polysaccharides from Russula vinosa lindblad extracted using KOH

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Journal of Bioresources and Bioproducts 2025年第2期10卷 253-269页

作者： Yan, Bowen Wu, Hao Zeng, Kui Huang, Caoxing Lai, Chenhuan Yong, Qiang Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources College of Chemical Engineering Nanjing Forestry University Nanjing210037 China College of Light Industry and Food Engineering Nanjing Forestry University Nanjing210037 China Department of Biomedical Engineering School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing211166 China School of Pharmacy University of Wisconsin-Madison Madison53705 United States

Russula vinosa Lindblad (R. vinosa Lindblad) is a nutrient-rich wild edible fungus, abundant in carbohydrates. In this study, two polysaccharides (Russula polysaccharides extracted from 1% KOH (RP-1) and Russula polysaccharides extracted from 5% KOH (RP-5)) were extracted from Russula fruit bodies using KOH-graded extraction technology. The molecular weights of RP-1 and RP-5 were 187 000 and 97 300 u, respectively. Their glycosyl compositions (galactose꞉glucose꞉xylose꞉ mannose, i.e., Gal꞉Glu꞉Xyl꞉Man) were 0.27:1.00:0.01:0.16 for RP-1 and 0.12:1.00:0.03:0.19 for RP-5. Nuclear magnetic resonance (NMR) analysis revealed that the main structural component of RP-1 was →3,6)-β-D-Glup-(1→, with a →6)-β-D-Glup-(1→ residue linked at sites 1 and 6, and a →3)-β-D-Glup-(1→ residue linked at sites 3. The RP-5 shared this structure with an additional →4)-β-D-Manp-(1→ residue attached to the →3)-β-D-Glup-(1→. Phagocytosis assay demonstrated that RP-5, with a lower molecular weight, lower galactose content, and higher xylose and mannose content, enhanced RAW 264.7 cell phagocytic activity by 121.04%, outperforming RP-1, which showed a 42.15% increase at the same concentration of 600 μg/mL. Furthermore, both RP-1 and RP-5 reduced the release of inflammatory factors and induced the nuclear translocation of the nuclear transcription factor-κB (NF-κB) pathway in RAW 264.7 cells. This study provided insights into the structural characteristics and immunomodulatory properties of Russula polysaccharides, offering potential for further applications in bioactive compound development. © 2025

关键词： Potassium hydroxide

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Cervical Cancer Histopathological Image Classification Using Imbalanced Domain Learning 11th

Cervical Cancer Histopathological Image Classification Using...

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11th EAI International Conference on Advancement of Science and Technology, ICAST 2023

作者： Simegn, Gizeaddis Lamesgin Degu, Mizanu Zelalem Tegenaw, Geletaw Sahle Biomedical Imaging Unit School of Biomedical Engineering Jimma Institute of Technology Jimma University Jimma Ethiopia AI and Biomedical Imaging Research Unit Jimma Institute of Technology Jimma University Jimma Ethiopia Faculty of computing and informatics Jimma Institute of Technology Jimma University Jimma Ethiopia

ISBN: (纸本)9783031641503

Early diagnosis of cervical cancer is challenging, especially in developing countries due to limited resource, infrastructure, and experts. Recent improvements in deep learning-based early cervical cancer detection techniques can potentially lower these challenges while increasing diagnosis accuracy. In this study, experiments were conducted on 465 histopathological images collected from patients with adenocarcinoma, squamous cell carcinoma, and squamous intraepithelial lesion. CNN and ResNet-50 models were trained for classification after the application of data augmentation, color normalization, and synthetic minority oversampling technique (SMOTE) on the data. Classification accuracies of 86.43% and 92.14% were found using CNN and ResNet-50 trained on raw data, respectively. ResNet-50 trained on a dataset "without a preprocessing technique," "affine transform-based data augmentation," "data augmentation and histogram match-based color normalization," "SMOTE," and "color normalization and SMOTE" achieved accuracies of 94.20%, 96.39%, 95.32%, 99.14%, and 90.56%, respectively. In general, the study investigated how image preprocessing, specifically data balancing, influences the effectiveness of deep learning models. Additionally, it examined the possibilities of utilizing computer-aided image processing to detect cervical cancer. The findings show that, with proper data processing, the proposed technique might be used as a cervical cancer screening tool, particularly in low-resource settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Affine transforms

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Effect of Metro Rhythmic Stimulation on Modifications of Walking with Poles – Case Study 21st

Effect of Metro Rhythmic Stimulation on Modifications of Wal...

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21st Scientific Conference on Medical and Sport Technologies, 2024

作者： Szaflik, Piotr Zadoń, Hanna Nowakowska-Lipiec, Katarzyna Wojtas, Marek Michnik, Robert Mitas, Andrzej W. Department of Biomechatronics Faculty of Biomedical Engineering Silesian University of Technology Gliwice Poland Department of Informatics and Medical Equipment Faculty of Biomedical Engineering Silesian University of Technology Gliwice Poland TELVIS Sp. Z O.O. Katowice Poland

ISBN: (纸本)9783031821424

Nordic Walking as well as walking with poles are two types of additional physical activity that have a beneficial effect on improving health and physical fitness. Both activities use poles, which primarily affect the change of asymmetry and stability of gait and also activate upper limb muscles and increase trunk involvement compared to physiological gait. Another beneficial factor influencing human gait is metrorhythmic stimulation, which favorably affects temporal and spatial parameters. Both stimuli are used in various diseases, such as Parkinson's disease and stroke, as a form of rehabilitation. Due to the beneficial effects, the authors of the article decided to combine the two and tested how the subjects respond to metrorhythmic stimulation during walking with poles and how this affects gait variability. Xsens Dot sensors and RAS4NoW software were used for the study. The study was conducted in real conditions - in a park, where walking with sticks is most often trained. In the first stage, the detection of the frequency of the steps taken was carried out, and then the research using metrorhythmic stimulation was started. Their frequency was successively 100%, 95%, and 105% of the frequency of steps taken during standard walking with poles. On the results of the study, it was observed that people who fit better in the metrorhythmic stimulation had lower values of gait variability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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