检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Cuffless Blood Pressure Estimation Based on PPG Features 6th

Cuffless Blood Pressure Estimation Based on PPG Features

引用

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

来源：评论

学校读者我要写书评

暂无评论

Development of a Waterproof EMG Sensor for Improved Signal Quality by Using Load Balancing

引用

IEEE Sensors Journal 2025年第9期25卷 14617-14628页

作者： Inoue, Yuki Yamanoi, Yusuke Kuroda, Yuki Ono, Yuma Shimada, Takayoshi Yabuki, Yoshiko Yokoi, Hiroshi The University of Electro-Communications Graduate School of Informatics and Engineering Department of Mechanical and Intelligent Engineering Tokyo1828585 Japan Tokyo University of Science Faculty of Engineering Department of Electrical Engineering Tokyo1258585 Japan OMRON SINIC X Corporation Tokyo1130033 Japan The University of Electro-Communications Graduate School of Informatics and Engineering Department of Mechanical and Intelligent Engineering Center for Neuroscience and Biomedical Engineering Tokyo1828585 Japan Mu-BORG K.K. Tokyo1828585 Japan

Our purpose is the development of an electromyogram (EMG) sensor that can be used underwater. EMG signals are incorrectly measured when water flows between the electrodes and the skin, preventing the use of myoelectric prosthetic hands underwater. Conventional waterproof sensors mainly focus on the waterproofness of the electrode and do not consider the stability of contact between the electrode and the skin. Thus, if water enters the area between the electrode and the skin, the acquired signal becomes unstable. We propose an EMG sensor system that uses a flexible wearable band to prevent water from entering between the electrode and the skin and stably measure EMGs. The waterproofness of the sensor is ensured by attaching the EMG sensor to the mesh-based silicone sheet and covering the sensor with silicone. Moreover, waterproofness between the skin and the sensor is ensured by applying tension to the flexible sensor band while the band deforms along the forearm, and the mesh-based silicone sheet around the electrode closely attaches to the skin. The evaluation results regarding the signal quality of the signal to noise ratio (SNR), median frequency (MDF), and attenuation rate in air and water confirmed that the same EMG signals could be acquired in both environments. These results indicate that we have succeeded in developing a sensor that can measure muscle activity in water, as the pressurization caused by the band enhances the sensor’s waterproofness. © 2001-2012 IEEE.

关键词： Electrodes Sensors Skin Electromyography Metals Prosthetic hand Rubber Muscles Presses Intelligent sensors

来源：评论

学校读者我要写书评

暂无评论

Continuous Blood Pressure Estimation Using PPG Signals Without Cuff 6th

Continuous Blood Pressure Estimation Using PPG Signals Witho...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

引用

Research on biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of biomedical engineering 2025.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

引用

Multimedia Tools and Applications 2025年第8期84卷 5349-5375页

作者： Shafique, Arslan Mehmood, Abid Alawida, Moatsum Elhadef, Mourad Rehman, Mujeeb Ur School of Biomedical Engineering University of Glasgow Glasgow United Kingdom Department of Computer Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Cyber Technology Institute School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications. © The Author(s) 2024.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：