Computer Methods in Biomechanics and Biomedical Engineering最新文献_第4页

A novel method in COPD diagnosing using respiratory signal generation based on CycleGAN and machine learning. 基于 CycleGAN 和机器学习的呼吸信号生成诊断慢性阻塞性肺病的新方法。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-07-01 Epub Date: 2024-03-29 DOI: 10.1080/10255842.2024.2329938

Kien Le Trung, Phuong Nguyen Anh, Trong-Thanh Han

{"title":"A novel method in COPD diagnosing using respiratory signal generation based on CycleGAN and machine learning.","authors":"Kien Le Trung, Phuong Nguyen Anh, Trong-Thanh Han","doi":"10.1080/10255842.2024.2329938","DOIUrl":"10.1080/10255842.2024.2329938","url":null,"abstract":"Objective: The main goal of this research is to use distinctive features in respiratory sounds for diagnosing Chronic Obstructive Pulmonary Disease (COPD). This study develops a classification method by utilizing inverse transforms to effectively identify COPD based on unique respiratory features while comparing the classification performance of various optimal algorithms.Method: Respiratory sounds are divided into individual breathing cycles. In the data standardization and augmentation phase, the CycleGAN model enhances data diversity. Comprehensive analyses for these segments are then implemented using various Wavelet families and different spectral transformations representing characteristic signals. Advanced convolutional neural networks, including VGG16, ResNet50, and InceptionV3, are used for the classification task.Results: The results of this study demonstrate the effectiveness of the mentioned method. Notably, the best-performing method utilizes Wavelet Bior1.3 after standardization in combination with InceptionV3, achieving a remarkable 99.75% F1-score, the gold standard for classification accuracy.Conclusion: Inverse transformation techniques combined with deep learning models show significant accuracy in detecting COPD disease. These findings suggest the feasibility of early COPD diagnosis through AI-powered characterization of acoustic features.Motivation and significance: The motivation behind this research stems from the urgent need for early and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD). COPD is a respiratory disease that poses many difficulties when detected late, potentially causing severe harm to the patient's quality of life and increasing the healthcare burden. Timely identification and intervention are crucial to reduce the progression of the disease and improve patient outcomes.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1538-1553"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140319775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

A hybrid approach to heart disease prediction using a fractional-order mathematical model and machine learning algorithm. 一种使用分数阶数学模型和机器学习算法的心脏病预测混合方法。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-26 DOI: 10.1080/10255842.2025.2523313

David Amilo, Khadijeh Sadri, Evren Hincal

{"title":"A hybrid approach to heart disease prediction using a fractional-order mathematical model and machine learning algorithm.","authors":"David Amilo, Khadijeh Sadri, Evren Hincal","doi":"10.1080/10255842.2025.2523313","DOIUrl":"https://doi.org/10.1080/10255842.2025.2523313","url":null,"abstract":"Heart disease remains one of the leading causes of morbidity and mortality worldwide, necessitating the development of more accurate and efficient diagnostic tools. This study presents a hybrid approach to heart disease prediction, combining fractional-order dynamics with decision tree algorithms and an interactive graphical user interface (GUI). Fractional-order models allow for a more elaborate representation of the complex physiological processes involved in heart disease, including factors such as cholesterol levels, blood pressure, inflammation, and plaque buildup. By integrating decision trees, a machine learning (ML) method known for its interpretability and efficiency in classification tasks, this approach enhances predictive accuracy. The use of an interactive GUI further enables healthcare professionals to visualize and interact with the model, providing real-time insights into patient risk profiles. The model's fractional-order differential equations (FDEs) account for varying rates of progression in different health parameters, offering a dynamic view of heart disease risk. Comprehensive simulations demonstrate the efficacy of the model, which outperforms traditional prediction models in terms of both accuracy and usability. The hybrid framework is intended to serve as a robust tool for clinicians, offering an innovative combination of advanced mathematical modeling and user-friendly machine-learning techniques for heart disease prediction. Our findings show that the decision tree classifier performed well, with 93% accuracy, 95% precision, 90% recall, and an F1-score of 0.92. The model handled non-linear relationships and missing data effectively, achieving an ROC-AUC score of 0.99. Key correlations, such as between ST depression and exercise-induced angina, were identified. Fractional-order simulations revealed how cholesterol, blood pressure, and other factors influenced heart disease risk, reinforcing clinical links through numerical simulations.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-30"},"PeriodicalIF":1.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Effect of seat configuration on the biodynamic response of the head-cervical spine during exposure to different frequencies of vertical vibration. 不同垂直振动频率下座椅结构对头-颈椎生物动力学响应的影响。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-24 DOI: 10.1080/10255842.2025.2523311

Yi-Tang Liu, Cheng-Ze Li, Jing-Fang Zhang, Rui-Chun Dong, Dao-Xi Liu, Qian Li, Bin Qi

{"title":"Effect of seat configuration on the biodynamic response of the head-cervical spine during exposure to different frequencies of vertical vibration.","authors":"Yi-Tang Liu, Cheng-Ze Li, Jing-Fang Zhang, Rui-Chun Dong, Dao-Xi Liu, Qian Li, Bin Qi","doi":"10.1080/10255842.2025.2523311","DOIUrl":"https://doi.org/10.1080/10255842.2025.2523311","url":null,"abstract":"The objective of this study is to investigate the effects of vertical vibration frequencies (4-10 Hz), back support, and cushion stiffness on the head-neck biodynamic responses based on a developed and validated finite element model of a body-seat system. Modal analysis and modal dynamics methods were employed to analyze the dynamic responses of the body-seat system under different conditions. The finite element model was used to examine the effects of various vibration frequencies (4-10 Hz), back support types (No back support (NBS) and Vertical back support (VBS)), and cushion stiffness (Elastic cushion (Soft) and Rigid cushion (Hard)) on the biodynamic responses of the head-neck. The body-seat system exhibited vertical resonance frequencies of 4.50, 6.50, 5.50, and 8.00 Hz for Soft-NBS, Soft-VBS, Hard-NBS, and Hard-VBS models, respectively, with vibration amplitude increasing near resonance. Vertical back support raised resonance frequency by 1.5-3.5 Hz and amplified head-neck vibration by 8.5-46.8% with a soft cushion, while reducing it by 14.5-55.9% with a hard cushion. Cushion hardness increased resonance frequency by 0.5-2.5 Hz and amplified head-neck vibration by 7.0-17.5% without back support, but reduced it by 7.0-33.1% with back support. Vertical back support and cushion stiffness significantly influence head-neck vibrations, especially near resonance frequencies. These findings highlight the importance of considering these factors in seat design to mitigate head-cervical injuries and enhance comfort and stability in vibration environments.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Comparative biomechanical analysis of Endo-LOVE and C-endo LFD techniques for bi-segmental cervical spondylotic radiculopathy in normal and osteoporotic patients: a finite element study. Endo-LOVE和C-endo LFD技术治疗正常和骨质疏松双节段神经根型颈椎病的比较生物力学分析：一项有限元研究

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-23 DOI: 10.1080/10255842.2025.2516174

Lei Ke, Jie Lan, Zepei Zhang, Shengrui Chu, Jun Miao, Xiaohui Li

引用次数: 0

A novel approach to venous clinical severity score prediction: combining metaheuristic algorithm and random forest classification. 一种新的静脉临床严重程度评分预测方法：结合meta启发式算法和随机森林分类。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-16 DOI: 10.1080/10255842.2025.2514133

Hao Zhu, Nianyun Zhang, Yuanzhen Ni, Qiang Sun

引用次数: 0

The role of shear micromotion on bone ingrowth into a three-dimensional porous scaffold predicted by a mechanoregulatary finite element model. 用力学调节有限元模型预测剪切微运动对骨长入三维多孔支架的作用。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-14 DOI: 10.1080/10255842.2025.2515478

Yunyang Gu, Alyssa G Oberman, Weidong Tong, Glen L Niebur

引用次数: 0

Midtarsal joint stiffness alters the metabolic cost of simulated running via mechanisms other than changes in foot energy storage and return. 跗骨中关节刚度通过一些机制改变模拟跑步的代谢成本，而不是通过足部能量储存和返回的变化。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-14 DOI: 10.1080/10255842.2025.2514794

Daniel J Davis, John H Challis

引用次数: 0

Prior knowledge guided logistic regression model with group lasso penalty for modeling epilepsy disease prediction. 基于先验知识的组套索惩罚逻辑回归模型用于癫痫疾病预测建模。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-14 DOI: 10.1080/10255842.2025.2515477

Xi Li, Yuanhua Qiao, Lijuan Duan, Jiang Du

引用次数: 0

Automated generation of implant-supported bar attachment system for patient-specific edentulous mandible: parametric modelling and finite element analysis. 患者无牙下颌骨种植体支撑杆附着系统的自动生成：参数化建模和有限元分析。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-12 DOI: 10.1080/10255842.2025.2516175

Hassen Jemaa, Michael Eisenburger, Michael-Tobias Neuhaus, Andreas Greuling

引用次数: 0

Advanced design and classification of wearable near-infrared spectroscopy device using temporal channel reconfiguration multi-graph convolutional neural networks for motor activity. 基于时间通道重构多图卷积神经网络的可穿戴近红外光谱装置的先进设计与分类。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-12 DOI: 10.1080/10255842.2025.2510370

V Akila, A Shirly Edward, J Anita Christaline

引用次数: 0