Computer Methods in Biomechanics and Biomedical Engineering最新文献

IPTGNet: an adaptive multi-task recognition strategy for human locomotion modes. IPTGNet：针对人类运动模式的自适应多任务识别策略。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-04 DOI: 10.1080/10255842.2025.2485366

Jing Tang, Lun Zhao, Minghu Wu, Zequan Jiang, Min Liu, Fan Zhang, Sheng Hu

引用次数: 0

Leveraging machine learning and bioinformatics to identify diagnostic biomarkers connected to hypoxia-related genes in preeclampsia. 利用机器学习和生物信息学识别与子痫前期缺氧相关基因有关的诊断生物标志物。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-03 DOI: 10.1080/10255842.2025.2484572

Jianfang Cao, Chaofen Zhou, Heshui Mao, Xia Zhang

{"title":"Leveraging machine learning and bioinformatics to identify diagnostic biomarkers connected to hypoxia-related genes in preeclampsia.","authors":"Jianfang Cao, Chaofen Zhou, Heshui Mao, Xia Zhang","doi":"10.1080/10255842.2025.2484572","DOIUrl":"https://doi.org/10.1080/10255842.2025.2484572","url":null,"abstract":"PE is a serious form of pregnancy-related hypertension. Hypoxia can induce cellular dysfunction, adversely affecting both the infant and the mother. This study aims to investigate the relationship between HRGs and the diagnosis of PE, seeking to enhance our understanding of potential molecular mechanisms and offer new perspectives for the detection and treatment of the condition. A WGCNA network was established to identify key genes significantly associated with traits of PE. LASSO, SVM-RFE, and RF were utilized to identify feature genes. Calibration curves and DCA were employed to assess the diagnostic performance of the comprehensive nomogram. Consensus clustering was applied to identify subtypes of PE. GSEA and the construction of a ceRNA network were used to explore the potential biological functions and regulatory mechanisms of the identified feature genes. Furthermore, ssGSEA was conducted to investigate the immune landscape associated with PE. We successfully identified three potential diagnostic biomarkers for PE: P4HA1, NDRG1, and BHLHE40. Furthermore, the nomogram exhibited strong diagnostic performance. In patients with PE, the abundance of pro-inflammatory immune cells was significantly elevated, reflecting characteristics of high infiltration. The levels of immune cells infiltration were significantly correlated with the expression of the identified feature genes. Notably, these feature genes may be closely linked to mitochondrial-related biological functions. In conclusion, our findings enhance the understanding of the pathological mechanisms underlying PE and open innovative avenues for the diagnosis and treatment of PE.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-19"},"PeriodicalIF":1.7,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781746","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

Lateral control of brain-controlled vehicle based on SVM probability output model.

IF 1.7 4区医学

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

Hongguang Pan, Hongzheng Gao, Zesheng Liu, Xinyu Yu

引用次数: 0

An adaptive graph convolutional network with residual attention for emotion recognition.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-02 DOI: 10.1080/10255842.2025.2484557

Dongrui Gao, Qingyuan Zheng, Pengrui Li, Manqing Wang

引用次数: 0

Stochastic intelligent computing solvers for the SIR dynamical prototype epidemic model using the impacts of the hospital bed. 利用病床影响的 SIR 动态原型流行病模型的随机智能计算求解器。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-01 Epub Date: 2024-01-02 DOI: 10.1080/10255842.2023.2300684

Manoj Gupta, Achyuth Sarkar

{"title":"Stochastic intelligent computing solvers for the SIR dynamical prototype epidemic model using the impacts of the hospital bed.","authors":"Manoj Gupta, Achyuth Sarkar","doi":"10.1080/10255842.2023.2300684","DOIUrl":"10.1080/10255842.2023.2300684","url":null,"abstract":"The present investigations are related to design a stochastic intelligent solver using the infrastructure of artificial neural networks (ANNs) and scaled conjugate gradient (SCG), i.e. ANNs-SCG for the numerical simulations of SIR dynamical prototype system based impacts of hospital bed. The SIR dynamical model is defined into three classes, susceptible patients in the hospital, infected population and recovered people. The proposed results are obtained through the sample statics of verification, testing and training of the dataset. The selection of the statics for training, testing and validation is chosen as 80%, 8% and 12%. A dataset is proposed based on the Adams scheme for the comparison of dynamical SIR prototype using the impacts of hospital bed. The numerical solutions are presented through the ANNs-SCG in order to reduce the values of the mean square error. To achieve the reliability, capability, accuracy, and competence of ANNs-SCG, the mathematical solutions are presented in the form of error histograms (EHs), regression, state transitions (STs) and correlation.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"655-667"},"PeriodicalIF":1.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081094","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

Biomechanical effects of different loads and constraints on finite element modeling of the humerus. 不同载荷和约束对肱骨有限元建模的生物力学影响。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-01 Epub Date: 2023-12-27 DOI: 10.1080/10255842.2023.2298371

Sabrina Islam, Kunal Gide, Emil H Schemitsch, Habiba Bougherara, Radovan Zdero, Z Shaghayegh Bagheri

引用次数: 0

EEG-BCI-based motor imagery classification using double attention convolutional network. 利用双注意卷积网络进行基于脑电图-BCI 的运动图像分类。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-01 Epub Date: 2024-01-02 DOI: 10.1080/10255842.2023.2298369

V Sireesha, V V Satyanarayana Tallapragada, M Naresh, G V Pradeep Kumar

{"title":"EEG-BCI-based motor imagery classification using double attention convolutional network.","authors":"V Sireesha, V V Satyanarayana Tallapragada, M Naresh, G V Pradeep Kumar","doi":"10.1080/10255842.2023.2298369","DOIUrl":"10.1080/10255842.2023.2298369","url":null,"abstract":"This article aims to improve and diversify signal processing techniques to execute a brain-computer interface (BCI) based on neurological phenomena observed when performing motor tasks using motor imagery (MI). The noise present in the original data, such as intermodulation noise, crosstalk, and other unwanted noise, is removed by Modify Least Mean Square (M-LMS) in the pre-processing stage. Traditional LMSs were unable to extract all the noise from the images. After pre-processing, the required features, such as statistical features, entropy features, etc., were extracted using Common Spatial Pattern (CSP) and Pearson's Correlation Coefficient (PCC) instead of the traditional single feature extraction model. The arithmetic optimization algorithm cannot select the features accurately and fails to reduce the feature dimensionality of the data. Thus, an Extended Arithmetic operation optimization (ExAo) algorithm is used to select the most significant attributes from the extracted features. The proposed model uses Double Attention Convolutional Neural Networks (DAttnConvNet) to classify the types of EEG signals based on optimal feature selection. Here, the attention mechanism is used to select and optimize the features to improve the classification accuracy and efficiency of the model. In EEG motor imagery datasets, the proposed model has been analyzed under class, which obtained an accuracy of 99.98% in class Baseline (B), 99.82% in class Imagined movement of a right fist (R) and 99.61% in class Imagined movement of both fists (RL). In the EEG dataset, the proposed model can obtain a high accuracy of 97.94% compared to EEG datasets of other models.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"581-600"},"PeriodicalIF":1.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075759","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 time segment adaptive optimization method based on separability criterion and correlation analysis for motor imagery BCIs. 基于可分性标准和相关性分析的时间段自适应优化方法，用于运动图像 BCI。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-01 Epub Date: 2024-01-09 DOI: 10.1080/10255842.2023.2301421

Lei Zhu, Mengxuan Xu, Jieping Zhu, Aiai Huang, Jianhai Zhang

{"title":"A time segment adaptive optimization method based on separability criterion and correlation analysis for motor imagery BCIs.","authors":"Lei Zhu, Mengxuan Xu, Jieping Zhu, Aiai Huang, Jianhai Zhang","doi":"10.1080/10255842.2023.2301421","DOIUrl":"10.1080/10255842.2023.2301421","url":null,"abstract":"Motor imagery (MI) plays a crucial role in brain-computer interface (BCI), and the classification of MI tasks using electroencephalogram (EEG) is currently under extensive investigation. During MI classification, individual differences among subjects in terms of response and time latency need to be considered. Optimizing the time segment for different subjects can enhance subsequent classification performance. In view of the individual differences of subjects in motor imagery tasks, this article proposes a Time Segment Adaptive Optimization method based on Separability criterion and Correlation analysis (TSAOSC). The fundamental principle of this method involves applying the separability criterion to various sizes of time windows within the training data, identifying the optimal raw reference signal, and adaptively adjusting the time segment position for each trial's data by analyzing its relationship with the optimal reference signal. We evaluated our method on three BCI competition datasets, respectively. The utilization of the TSAOSC method in the experiments resulted in an enhancement of 4.90% in average classification accuracy compared to its absence. Additionally, building upon the TSAOSC approach, this study proposes a Nonlinear-TSAOSC method (N-TSAOSC) for analyzing EEG signals with nonlinearity, which shows improvements in the classification accuracy of certain subjects. The results of the experiments demonstrate that the proposed method is an effective time segment optimization method, and it can be integrated into other algorithms to further improve their accuracy.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"710-723"},"PeriodicalIF":1.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139405107","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

Simulation study on the force-electric effect of piezoelectric bone and osteocytes under static and dynamic compression.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-01 DOI: 10.1080/10255842.2025.2484562

Zhu Wang, Haiying Liu, Hanqing Zhao, Chunqiu Zhang

引用次数: 0

A scale conjugate neural network approach for the fractional schistosomiasis disease system. 分型血吸虫病系统的尺度共轭神经网络方法。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-01 Epub Date: 2023-12-26 DOI: 10.1080/10255842.2023.2298717

Zulqurnain Sabir, Shahid Ahmad Bhat, Muhammad Asif Zahoor Raja, Dumitru Baleanu, Fazli Amin, Hafiz Abdul Wahab

{"title":"A scale conjugate neural network approach for the fractional schistosomiasis disease system.","authors":"Zulqurnain Sabir, Shahid Ahmad Bhat, Muhammad Asif Zahoor Raja, Dumitru Baleanu, Fazli Amin, Hafiz Abdul Wahab","doi":"10.1080/10255842.2023.2298717","DOIUrl":"10.1080/10255842.2023.2298717","url":null,"abstract":"This study presents the numerical solutions of the fractional schistosomiasis disease model (SDM) using the supervised neural networks (SNNs) and the computational scaled conjugate gradient (SCG), i.e. SNNs-SCG. The fractional derivatives are used for the precise outcomes of the fractional SDM. The preliminary fractional SDM is categorized as: uninfected, infected with schistosomiasis, recovered through infection, expose and susceptible to this virus. The accurateness of the SNNs-SCG is performed to solve three different scenarios based on the fractional SDM with synthetic data obtained with fractional Adams scheme (FAS). The generated data of FAS is used to execute SNNs-SCG scheme with 81% for training samples, 12% for testing and 7% for validation or authorization. The correctness of SNNs-SCG approach is perceived by the comparison with reference FAS results. The performances based on the error histograms (EHs), absolute error, MSE, regression, state transitions (STs) and correlation accomplish the accuracy, competence, and finesse of the SNNs-SCG scheme.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"614-627"},"PeriodicalIF":1.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139040855","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