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Unveiling fetal heart health: harnessing auto-metric graph neural networks and Hazelnut tree search for ECG-based arrhythmia detection. 揭示胎儿心脏健康：利用自度量图神经网络和榛子树搜索心电图为基础的心律失常检测。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-24 DOI: 10.1080/10255842.2025.2481232
M Suganthy, B Sarala, G Sumathy, W T Chembian
{"title":"Unveiling fetal heart health: harnessing auto-metric graph neural networks and Hazelnut tree search for ECG-based arrhythmia detection.","authors":"M Suganthy, B Sarala, G Sumathy, W T Chembian","doi":"10.1080/10255842.2025.2481232","DOIUrl":"https://doi.org/10.1080/10255842.2025.2481232","url":null,"abstract":"<p><p>Fetal electrocardiogram (ECG) provides a non-invasive means to assess fetal heart health, but isolating the fetal signal from the dominant maternal ECG remains challenging. This study introduces the FHH-AMGNN-HTSOA-ECG-AD method for enhanced fetal arrhythmia detection. It employs Dual Tree Complex Wavelet Transform for denoising and utilizes an Auto-Metric Graph Neural Network (AMGNN) optimized by the Hazelnut Tree Search Algorithm (HTSOA). This integration enables accurate classification of normal and abnormal fetal heart signals. Experimental results demonstrate that the proposed approach significantly outperforms existing methods in terms of accuracy, precision, and specificity.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044115","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
Identification of immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies. 应用机器学习策略识别颅内动脉瘤伴蛛网膜下腔出血的免疫相关生物标志物和免疫浸润。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-23 DOI: 10.1080/10255842.2025.2495250
Xiao Jin, Xiang Zhao
{"title":"Identification of immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies.","authors":"Xiao Jin, Xiang Zhao","doi":"10.1080/10255842.2025.2495250","DOIUrl":"https://doi.org/10.1080/10255842.2025.2495250","url":null,"abstract":"<p><p><b>Background:</b> Subarachnoid hemorrhage (SAH) risk increases with intracranial aneurysms (IA), but their relationship remains unclear. <b>Methods:</b> We explored SAH-IA links using machine learning and bioinformatics, identifying 66 IA-related SAH genes. KEGG analysis highlighted pathways like NF-κB, TNF, and COVID-19. <b>Results:</b> Two immune-related genes (ZNF281, LRRN3) were identified, and a ceRNA network was constructed. Ten potential SAH-IA drugs were screened via CMAP. <b>Conclusion:</b> ZNF281 and LRRN3 may regulate immune pathways (T cells, NK cells, macrophages), influencing IA-related SAH development, and could serve as therapeutic targets.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065181","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
The biomechanical injury calculator: a postprocessor software for a finite element human body model. 生物力学损伤计算器：一种有限元人体模型的后处理软件。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-23 DOI: 10.1080/10255842.2024.2448554
Srihari Menon, Quenton Hua, Nancy J Currie-Gregg
{"title":"The biomechanical injury calculator: a postprocessor software for a finite element human body model.","authors":"Srihari Menon, Quenton Hua, Nancy J Currie-Gregg","doi":"10.1080/10255842.2024.2448554","DOIUrl":"https://doi.org/10.1080/10255842.2024.2448554","url":null,"abstract":"<p><p>An injury risk assessment postprocessor for the Global Human Body Model Consortium (GHBMC) model is presented. The Biomechanical Injury Calculator (BIC) calculates injury probabilities for the head, neck, spine, and pelvis post-simulation, along with a total injury probability for the entire complex. It also generates an injury heatmap. Developed for the GHBMC M50-OS v2.3 +DeformSpine, BIC was validated by comparing 103 airmen's seat ejection injuries to BIC-predicted injury probabilities in 30 vertical seat load simulations. Observed injury rates correlated strongly with BIC predictions (Spearman=0.943, Pearson=0.982) within 5.16% margin. The total injury probability of 58.48% closely matched the 56.3% observed rate.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044113","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
Epileptic seizure detection in EEG signals using deep learning: LSTM and bidirectional LSTM. 基于深度学习的脑电信号癫痫发作检测：LSTM和双向LSTM。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-21 DOI: 10.1080/10255842.2025.2490136
Ghezala Chekhmane, Radhwane Benali
{"title":"Epileptic seizure detection in EEG signals using deep learning: LSTM and bidirectional LSTM.","authors":"Ghezala Chekhmane, Radhwane Benali","doi":"10.1080/10255842.2025.2490136","DOIUrl":"https://doi.org/10.1080/10255842.2025.2490136","url":null,"abstract":"<p><p>This paper established a new automatic method to detect epileptic seizures in EEG signals based on discret wavelet transform (DWT) and Deep Learning (DL). DWT is used to decompose EEG into different sub-bands. Moreover, the proposed model combines Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) networks with one layer of each network consecutive. The experimental results yield higher accuracies of 100% which it is demonstrated that the obtained results achieve better performance by using the new hybrid LSTM-BiLSTM network than other works. Finally, this hybrid LSTM-BiLSTM model confirmed their effectiveness for the classification of epileptic EEG signals.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-24"},"PeriodicalIF":1.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057586","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 deep learning framework for enhanced mass spectrometry data analysis and biomarker screening. 用于增强质谱数据分析和生物标志物筛选的深度学习框架。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-15 DOI: 10.1080/10255842.2025.2488501
Shuyu Zhang, Zhiyu Li, Weili Peng, Yuanyuan Chen, Yao Wu
{"title":"A deep learning framework for enhanced mass spectrometry data analysis and biomarker screening.","authors":"Shuyu Zhang, Zhiyu Li, Weili Peng, Yuanyuan Chen, Yao Wu","doi":"10.1080/10255842.2025.2488501","DOIUrl":"https://doi.org/10.1080/10255842.2025.2488501","url":null,"abstract":"<p><p>Mass spectrometry (MS) serves as a powerful analytical technique in metabolomics. Traditional MS analysis workflows are heavily reliant on operator experience and are prone to be influenced by complex, high-dimensional MS data. This study introduces a deep learning framework designed to enhance the classification of complex MS data and facilitate biomarker screening. The proposed framework integrates preprocessing, classification, and biomarker selection, addressing challenges in high-dimensional MS analysis. Experimental results demonstrate significant improvements in classification tasks compared to other machine learning approaches. Additionally, the proposed peak-preprocessing module is validated for its potential in biomarker screening, identifying potential biomarkers from high-dimensional data.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006443","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 foot-shaped bionic shoes on ground reaction forces and foot stress at various running speeds. 仿脚鞋对不同跑步速度下地面反作用力和足部应力的影响。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-15 DOI: 10.1080/10255842.2025.2490139
Shunxiang Gao, Dong Sun, Yang Song, Xuanzhen Cen, Qiaolin Zhang, Zixiang Gao, Zhiyi Zheng, Monèm Jemni, Yaodong Gu
{"title":"Effect of foot-shaped bionic shoes on ground reaction forces and foot stress at various running speeds.","authors":"Shunxiang Gao, Dong Sun, Yang Song, Xuanzhen Cen, Qiaolin Zhang, Zixiang Gao, Zhiyi Zheng, Monèm Jemni, Yaodong Gu","doi":"10.1080/10255842.2025.2490139","DOIUrl":"https://doi.org/10.1080/10255842.2025.2490139","url":null,"abstract":"<p><p>This study examined ground reaction forces(GRFs) and bone stress differences between bionic running shoes (with foot-mimicking soles) and traditional shoes during running.Sixteen experienced male runners ran at 10, 12, and 14 km/h in both shoe types. Two-way ANOVA and SPM1d showed that bionic shoes had significantly lower peak propulsive but higher peak braking forces than traditional shoes.Bionic shoes also exhibited lower vertical forces in early stance and altered anterior-posterior forces patterns in late stance; finite element analysis indicated lower metatarsal stress in the bionic midsoles. These findings provide insights for designing footwear to prevent running injuries.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-11"},"PeriodicalIF":1.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051169","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 finite element study of the biomechanics of aging osteocyte model. 老化骨细胞模型生物力学的有限元研究。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-14 DOI: 10.1080/10255842.2025.2479853
Xiaogang Wu, Jiajun Wang, Zhengbiao Yang, Yanru Xue, Meng Zhang, Jing Chen, Pengcui Li, Yanqin Wang, Yongxing Wang, Xiyu Wang, Weiyi Chen, Xiaochun Wei
{"title":"A finite element study of the biomechanics of aging osteocyte model.","authors":"Xiaogang Wu, Jiajun Wang, Zhengbiao Yang, Yanru Xue, Meng Zhang, Jing Chen, Pengcui Li, Yanqin Wang, Yongxing Wang, Xiyu Wang, Weiyi Chen, Xiaochun Wei","doi":"10.1080/10255842.2025.2479853","DOIUrl":"https://doi.org/10.1080/10255842.2025.2479853","url":null,"abstract":"<p><p>Aging lacuna-canalicular system (LCS) has osteocyte size reduction, cell process number loss, and canaliculus blockage. This study built four osteocyte aging models for various aging features. These models have processes, collagen hillocks, and primary cilia mechanoreceptors for signal comparison. A triaxial displacement load on the piezoelectric bone matrix was utilized to explore mechanical signal changes in the absence of different processes, canaliculi blockage, and the effects of aging on osteocyte mechanoreceptor signals. Osteocyte age doesn't affect piezoelectric effect electric field strength. The aging model flows slower than the normal model. Blocking canaliculi raises fluid pressure. Aging osteocytes lack processes, reducing primary cilia and process stress-strain. Osteocyte volume reduction and canaliculi blockage exacerbate this alteration.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023328","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
Cardiotocography data analysis for foetal health classification using Spatial Bayesian Neural Network Optimized with Dwarf Mongoose Optimizer. 基于空间贝叶斯神经网络的心脏造影胎儿健康分类分析。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-10 DOI: 10.1080/10255842.2025.2478293
P Solainayagi, G Sivagaminathan, Sabenabanu Abdulkadhar, A Gnana Soundari, K Krishnakumar
{"title":"Cardiotocography data analysis for foetal health classification using Spatial Bayesian Neural Network Optimized with Dwarf Mongoose Optimizer.","authors":"P Solainayagi, G Sivagaminathan, Sabenabanu Abdulkadhar, A Gnana Soundari, K Krishnakumar","doi":"10.1080/10255842.2025.2478293","DOIUrl":"https://doi.org/10.1080/10255842.2025.2478293","url":null,"abstract":"<p><p>Pregnancy complications require early detection, but traditional Cardiotocography (CTG) analysis is labor-intensive and error-prone. This manuscript presents Cardiotocography Data Analysis for Foetal Health Classification using Spatial Bayesian Neural Network Optimized with Dwarf Mongoose Optimizer (CDA-FHC-SBNN-DMO). The process involves collecting CTG data, optimizing feature selection with Humboldt Squid Optimization Algorithm (HSOA) and classification using Spatial Bayesian Neural Network (SBNN) to categorize foetal health. Dwarf Mongoose Optimizer (DMO) is used to optimize SBNN. The CDA-FHC-SBNN-DMO method was implemented in Python, outperforms existing methods, achieving improvements of 20.89%, 31.45%, and 28.32% in accuracy, and significant increases in precision and recall.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144038760","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
EEG-based multi-band functional connectivity using corrected amplitude envelope correlation for identifying unfavorable driving states. 基于脑电图的多波段功能连接，使用校正振幅包络相关来识别不利的驾驶状态。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-09 DOI: 10.1080/10255842.2025.2488502
Jichi Chen, Yujie Wang, Yuguo Cui, Hong Wang, Kemal Polat, Fayadh Alenezi
{"title":"EEG-based multi-band functional connectivity using corrected amplitude envelope correlation for identifying unfavorable driving states.","authors":"Jichi Chen, Yujie Wang, Yuguo Cui, Hong Wang, Kemal Polat, Fayadh Alenezi","doi":"10.1080/10255842.2025.2488502","DOIUrl":"https://doi.org/10.1080/10255842.2025.2488502","url":null,"abstract":"<p><p>Recognition of unfavorable driving state (UDS) based on Electroencephalography (EEG) signals and functional connectivity has a significant contribution to reducing casualties. However, when the functional connectivity approach directly applies to recognize drivers' UDS, it may encounter great challenges, because of spurious synchronization phenomenon. We introduce a novel functional connectivity matrix construction approach combined with the ensemble algorithm to identify drivers' UDS in the research. First, EEG data from a previously designed simulated driving experiment containing two driving tasks are extracted, and then functional connectivity matrix construction approach based on amplitude envelope correlation with leakage correction (AEC-c) in multiple frequency bands are calculated. Furthermore, the random subspace is utilized to improve the performances of the k-nearest neighbors (KNN) algorithm. Classification performances of the proposed approach are assessed by confusion matrix, accuracy (ACC), sensitivity (SEN), specificity (SPF), precision (PRE) and receiver operating characteristic (ROC) curve with 5-fold cross-validation strategy. The statistical analysis shows that the regional AEC-c values of 30 EEG channels for the driver's UDS are overall significantly lower than those for the driver's non-unfavorable driving state (NUDS) in the beta, gamma and all frequency bands. Further analysis about performance results shows that the proposed AEC-c-based functional connection matrix analysis approach in all frequency bands combined with the random subspace ensembles KNN achieves a highest ACC of 96.88%. The results suggests that our proposed framework is beneficial for EEG-based driver's UDS recognition, which is helpful to the transmission and interaction of information in man-machine system.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046451","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
The influence of knee varus deformity on the kinetic and kinematic characteristics of musculoskeletal models during gait. 膝关节内翻畸形对步态中肌肉骨骼模型动力学和运动学特性的影响。
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-09 DOI: 10.1080/10255842.2025.2487817
Sina Tabeiy, Morad Karimpour, Azizollah Shirvani, Arash SharafatVaziri
{"title":"The influence of knee varus deformity on the kinetic and kinematic characteristics of musculoskeletal models during gait.","authors":"Sina Tabeiy, Morad Karimpour, Azizollah Shirvani, Arash SharafatVaziri","doi":"10.1080/10255842.2025.2487817","DOIUrl":"https://doi.org/10.1080/10255842.2025.2487817","url":null,"abstract":"<p><p>An inherent shortcoming of generic musculoskeletal models is their inability to accurately model deformed musculoskeletal system, while generating fully personalized models are time and money consuming. To address this, we created a MATLAB tool that modifies OpenSim generic models, producing semi-personalized models. A subject with varus deformity participated in our study and her associated semi-personalized model was created. The semi-personalized model significantly decreased marker error during Inverse Kinematics (<i>p</i> < 0.05). Notably, hip abduction/adduction ROM increased by 4.87 degrees on average, muscle activation in TFL rose by 53%, and Gracilis activation dropped by 4% in the semi-personalized model. Overall, semi-personalized models enhance accuracy for higher musculoskeletal analyses.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-8"},"PeriodicalIF":1.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056575","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
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