Computer Methods in Biomechanics and Biomedical Engineering最新文献_第10页

Plate materials for cardiopulmonary protection: a computational modeling study. 用于心肺保护的钢板材料：一项计算模型研究。

IF 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-03-17 DOI: 10.1080/10255842.2025.2476192

Maayan Lustig, Yoram Epstein, Amit Gefen

{"title":"Plate materials for cardiopulmonary protection: a computational modeling study.","authors":"Maayan Lustig, Yoram Epstein, Amit Gefen","doi":"10.1080/10255842.2025.2476192","DOIUrl":"10.1080/10255842.2025.2476192","url":null,"abstract":"Personal body armor, such as ballistic protective plates (BPPs), plays a vital role in protecting the torso against high-energy impacts, yet Behind Armor Blunt Trauma (BABT) remains a concern. BABT can inflict damage on critical organs, particularly the heart and lungs. This study investigates the protective performance of BPP materials and padding configurations. We employed a finite element (FE) model of the torso, incorporating detailed anatomical features, to simulate non-penetrating impacts on the heart and lungs. Three BPP materials - Kevlar-29, Ultra-High-Molecular-Weight Polyethylene (UHMWPE), and Alumina, were analyzed with and without 6 mm and 12 mm padding layers against a 5.56 mm bullet impact at 500 m/s. The results demonstrated that Alumina plates provided superior protection, resulting in 186% lower peak strain and 229% lower peak stress in the heart compared to Kevlar and UHMWPE. The addition of padding further reduced strains and stresses, with 12 mm padding yielding average reductions of 44% in peak strain and 36% in peak stress in the heart. Similarly, in the lungs, 12 mm padding led to reductions of 38% in peak strain and 34% in peak stress. The model was validated against experimental force measurements using a life-sized torso phantom, demonstrating strong agreement with piezoelectric sensor measurements (less than 4% difference). These findings underscore the importance of selecting appropriate BPP materials and padding thickness to minimize biomechanical impacts on vital organs.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"2319-2341"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651689","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 emotion recognition with autoencoder feature fusion and MSC-TimesNet model. 基于自编码器特征融合和MSC-TimesNet模型的脑电图情感识别。

IF 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-03-17 DOI: 10.1080/10255842.2025.2477801

Jibin Yin, Zhijian Qiao, Luyao Han, Xiangliang Zhang

{"title":"EEG-based emotion recognition with autoencoder feature fusion and MSC-TimesNet model.","authors":"Jibin Yin, Zhijian Qiao, Luyao Han, Xiangliang Zhang","doi":"10.1080/10255842.2025.2477801","DOIUrl":"10.1080/10255842.2025.2477801","url":null,"abstract":"Electroencephalography (EEG) signals are widely employed due to their spontaneity and robustness against artifacts in emotion recognition. However, existing methods are often unable to fully integrate high-dimensional features and capture changing patterns in time series when processing EEG signals, which results in limited classification performance. This paper proposes an emotion recognition method (AEF-DL) based on autoencoder fusion features and MSC-TimesNet models. Firstly, we segment the EEG signal in five frequency bands into time windows of 0.5 s, extract power spectral density (PSD) features and differential entropy (DE) features, and implement feature fusion using the autoencoder to enhance feature representation. Based on the TimesNet model and incorporating the multi-scale convolutional kernels, this paper proposes an innovative deep learning model (MSC-TimesNet) for processing fused features. MSC-TimesNet efficiently extracts inter-period and intra-period information. To validate the performance of the proposed method, we conducted systematic experiments on the public datasets DEAP and Dreamer. In dependent experiments with subjects, the classification accuracies reached 98.97% and 95.71%, respectively; in independent experiments with subjects, the accuracies reached 97.23% and 92.95%, respectively. These results demonstrate that the proposed method exhibits significant advantages over existing methods, highlighting its effectiveness and broad applicability in emotion recognition tasks.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"2397-2414"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651644","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

Advancing cancer care: unravelling genomic insights for precision medicine using meticulous predictive architecture. 推进癌症护理：利用细致的预测架构揭示精准医学的基因组见解。

IF 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-03-31 DOI: 10.1080/10255842.2025.2477809

Swati B Bhonde, Sharmila K Wagh, Jayashree R Prasad

引用次数: 0

Identification of DYRK2 and TRIM32 as keloids programmed cell death-related biomarkers: insights from bioinformatics and machine learning in multiple cohorts. 鉴定DYRK2和TRIM32作为瘢痕疙瘩程序性细胞死亡相关的生物标志物：来自多队列生物信息学和机器学习的见解。

IF 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-03-24 DOI: 10.1080/10255842.2025.2482129

Xi Yang, Yao Yang, Mingjian Zhao, He Bai, Chongyang Fu

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 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-04-15 DOI: 10.1080/10255842.2025.2488501

Shuyu Zhang, Zhiyu Li, Weili Peng, Yuanyuan Chen, Yao Wu

引用次数: 0

Lateral control of brain-controlled vehicle based on SVM probability output model. 基于SVM概率输出模型的脑控车辆横向控制。

IF 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-04-02 DOI: 10.1080/10255842.2025.2484565

Hongguang Pan, Hongzheng Gao, Zesheng Liu, Xinyu Yu

引用次数: 0

The influence of cement-bone composite material on the biomechanical properties of cervical partial vertebral osteotomy fusion surgery. 骨水泥复合材料对颈椎部分椎体截骨融合手术生物力学性能的影响。

IF 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-03-13 DOI: 10.1080/10255842.2025.2477208

Dong-Xiang Zhang, Li-Xin Guo

引用次数: 0

Optimized dense back-projection joint attention network for automatic seizure detection from EEG signal. 基于脑电信号的癫痫发作自动检测的优化密集背投影联合注意网络。

IF 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-11-17 DOI: 10.1080/10255842.2025.2476179

Mohandass G, Sridhathan C, Hari Krishnan G, Nandhini I

引用次数: 0

A neural network model based on attention pooling and adaptive multi-level feature fusion for arrhythmia automatic detection. 基于注意力池和自适应多层次特征融合的心律失常自动检测神经网络模型。

IF 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-03-20 DOI: 10.1080/10255842.2025.2480264

Yushuai Wang, Hao Dong, Haitao Wu, Wenqi Wang, Junming Zhang

{"title":"A neural network model based on attention pooling and adaptive multi-level feature fusion for arrhythmia automatic detection.","authors":"Yushuai Wang, Hao Dong, Haitao Wu, Wenqi Wang, Junming Zhang","doi":"10.1080/10255842.2025.2480264","DOIUrl":"10.1080/10255842.2025.2480264","url":null,"abstract":"With the rising incidence of cardiovascular disease, timely detection and treatment are critical for patients with arrhythmias, and the electrocardiogram (ECG) remains a vital tool for diagnosing and monitoring heart health. In automated arrhythmia detection, researchers have made significant progress in intra-patient paradigms. However, challenges persist in the inter-patient paradigm, where existing methods often rely on manually extracted features or exhibit inadequate performance in detecting anomalous categories. Against the above challenges, this paper proposes a neural network model based on Attention Pooling (AP) and Adaptive Multilevel Feature Fusion (AMFF) to enhance the performance for automatic detection of abnormal categories in the inter-patient paradigm. Among them, the attentional pooling mechanism enables the model to focus on the features of key channels and spatial locations, effectively reducing the influence of redundant information; to address the problem of ECG signal scale differences, we designed adaptive multilevel feature fusion (AMFF), which uses weighted multilevel features to achieve adaptive feature fusion and can utilize multilevel features at the same time, thus enhancing the feature expression capability of the model. Based on following the AAMI criteria, we evaluated the proposed model using the MIT-BIH arrhythmia database. The results showed that the model achieved an overall accuracy of 99.32% in the intra-patient paradigm and 93.35% in the inter-patient paradigm. For the inter-patient paradigm, the model not only performs well in N-category classification but also achieves good results in the anomaly categories of S, V, and F. This demonstrates a relatively balanced performance.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"2534-2548"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665231","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 1.9 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-08-01 Epub Date: 2025-04-09 DOI: 10.1080/10255842.2025.2487817

Sina Tabeiy, Morad Karimpour, Azizollah Shirvani, Arash SharafatVaziri

引用次数: 0