Computer Methods in Biomechanics and Biomedical Engineering最新文献_第3页

Motor imagery EEG microstates are influenced by alpha power. 运动想象的脑电图微观状态受阿尔法功率的影响。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-24 DOI: 10.1080/10255842.2025.2476185

Xin Xiong, Xiaoyu Ji, Sanli Yi, Chunwu Wang, Ruixiang Liu, Jianfeng He

{"title":"Motor imagery EEG microstates are influenced by alpha power.","authors":"Xin Xiong, Xiaoyu Ji, Sanli Yi, Chunwu Wang, Ruixiang Liu, Jianfeng He","doi":"10.1080/10255842.2025.2476185","DOIUrl":"https://doi.org/10.1080/10255842.2025.2476185","url":null,"abstract":"Electroencephalogram (EEG) microstates are pivotal in understanding brain dynamics, reflecting transitions between global states. These parameters undergo selective inhibition within cortical areas, modulated by alpha oscillations. This study investigates how alpha band power influences microstate parameters across various task conditions, including resting state, actual motor execution, and imagined motor tasks. By comparing these three conditions, we aim to elucidate the distinct effects of alpha power on microstate dynamics, as each condition represents a unique pattern of brain activity. Motor imagery (MI) induces event-related desynchronization/synchronization, modulating Mu (alpha) and Beta rhythms in sensorimotor areas. However, the relationship between MI-EEG microstates and alpha power remains unclear. Our results show that alpha power was highest in resting state, followed by imagined motion, and lowest during actual motion. As alpha power increased, microstate A parameters in resting state (occurrence, coverage) decreased, while those in actual motion increased. Additionally, microstate B parameters rose with alpha power in resting state but decreased during imagined motion. Notably, alpha power correlated more strongly with microstate parameters in task states than in resting state. In addition, alpha, theta, and beta powers during task performance were negatively correlated with the duration of microstates A, B, and C, while being positively correlated with the occurrence of microstates A, B, C, and D. These findings suggest that alpha power influences microstate parameters differently depending on the brain, underscoring the significance of inter-band interactions in shaping microstate dynamics.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694023","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 DYRK2 and TRIM32 as keloids programmed cell death-related biomarkers: insights from bioinformatics and machine learning in multiple cohorts.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub 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

Development and nursing application of kidney disease prediction models based on machine learning.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-24 DOI: 10.1080/10255842.2025.2479856

Yan Zhang, Hui Gao

引用次数: 0

Transforming wearable sensor data for robust feature selection in human activity recognition using reinforcement learning approach. 利用强化学习方法转换可穿戴传感器数据，在人类活动识别中实现稳健的特征选择。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-24 DOI: 10.1080/10255842.2025.2480686

Ravi Kumar Athota, D Sumathi

引用次数: 0

Modelling receptor-mediated endocytosis in hollow microneedle-based verapamil delivery through viscoelastic skin.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1080/10255842.2025.2477223

Tanmoy Bhuimali, Sarifuddin, Prashanta Kumar Mandal

{"title":"Modelling receptor-mediated endocytosis in hollow microneedle-based verapamil delivery through viscoelastic skin.","authors":"Tanmoy Bhuimali, Sarifuddin, Prashanta Kumar Mandal","doi":"10.1080/10255842.2025.2477223","DOIUrl":"https://doi.org/10.1080/10255842.2025.2477223","url":null,"abstract":"Drug delivered from the microneedle (MN) tip diffuses across the viscoelastic skin before entering the blood compartment and being absorbed. Reversible uptake kinetics between the blood and tissue compartments, reversible specific saturable binding with its receptors, and endocytosis are given due attention. Simulations predict that, unlike skin thinning, skin viscoelasticity and a higher Young's modulus value, as in an older person, inhibit verapamil diffusion within the skin, and metabolism stabilises the concentrations in the blood and tissue compartments. Simultaneously, the irreversible uptake kinetics improve drug concentrations in the tissue compartment, facilitating receptor-mediated endocytosis. The results also predict that internalised verapamil increases with time at slower internalisation rates; however, at higher rates, it attains a peak value before gradually diminishing. Furthermore, as the rate of lysosomal degradation escalates, the peak value of internalised concentration diminishes and shifts upward. A comprehensive sensitivity analysis has been performed because of uncertainty about several crucial parameters. Our findings align well with the existing literature.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-19"},"PeriodicalIF":1.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665139","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

lncRNA-disease association prediction based on optimizing measures of multi-graph regularized matrix factorization.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1080/10255842.2025.2479854

Bin Yao, Yunzhong Song

引用次数: 0

Assessment of atherosclerosis risk in an insufficient sample size based on K-Means BS and TW-gcForest.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1080/10255842.2025.2475478

Yudong Zhang, Wenjun Liu, Lidan He, Mengdie Yang, Hui Huang

{"title":"Assessment of atherosclerosis risk in an insufficient sample size based on K-Means BS and TW-gcForest.","authors":"Yudong Zhang, Wenjun Liu, Lidan He, Mengdie Yang, Hui Huang","doi":"10.1080/10255842.2025.2475478","DOIUrl":"https://doi.org/10.1080/10255842.2025.2475478","url":null,"abstract":"Background: Insufficient data is a common issue encountered in studies of atherosclerosis risk assessment. However, when the sample size is insufficient, commonly used classification algorithms often fail to achieve superior performance, thereby limiting the application of atherosclerosis data in patient risk assessment.Purpose: In cases where the sample size is inadequate, the use of an algorithmic model can allow for an effective evaluation of the risk of atherosclerosis in patients.Methods: We propose an oversampling technique called K-Means-Borderline-SMOTE (K-Means BS) and a classification algorithm named triple-weighted gcForest (TW-gcForest). Our proposed K-Means BS generates diverse synthetic samples by imposing strict constraints and avoids generating similar synthetic samples. TW-gcForest is mainly designed to address the problem of unfair forest weight allocation and sliding window weight allocation in standard gcForest. We perform numerical simulations on two datasets to demonstrate the robustness of the two methods.Results: Numerical simulations show that standard gcForest achieves the high-performance for atherosclerosis risk assessment on the K-Means BS synthetic dataset. However, TW-gcForest exhibits superior performance to the standard gcForest on the original dataset, as well as the SMOTE and K-Means BS synthetic datasets.Conclusion: Our approach can effectively improve accuracy, precision, recall, F1 score, and AUC compared with traditional algorithms.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665236","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 neural network model based on attention pooling and adaptive multi-level feature fusion for arrhythmia automatic detection.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub 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":"https://doi.org/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":"1-15"},"PeriodicalIF":1.7,"publicationDate":"2025-03-20","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

Correspondence model-based approach for evaluating static and dynamic joint distance measurements.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-20 DOI: 10.1080/10255842.2025.2478527

Rich J Lisonbee, Andrew C Peterson, Amy L Lenz

{"title":"Correspondence model-based approach for evaluating static and dynamic joint distance measurements.","authors":"Rich J Lisonbee, Andrew C Peterson, Amy L Lenz","doi":"10.1080/10255842.2025.2478527","DOIUrl":"https://doi.org/10.1080/10255842.2025.2478527","url":null,"abstract":"Evaluations of 3D joint space measurements between study groups have traditionally relied on surface regional divisions, which attenuate the impact of shape on joint measurements. Advancements in morphometric analyses have enabled evaluation of population-based shape variations as they relate to disease progression and deformity. Specifically, correspondence model-based shape analyses offer co-registered landmarks that address shape variability in joint structures and can be utilized for comparison of joint space measurements. This study proposes a method using correspondence models to perform group-wise statistical analyses in static or quasi-static positions during movement, offering a more comprehensive assessment of joint space variability. The primary objective was to verify and validate the measurement methods of a developed open-source toolbox. Testing was performed with surface meshes of varying edge length (0.5-, 1-, and 2-mm) and with different expected joint space distances (1- and 4-mm). Validation testing of accuracy revealed <1% error for 0.5- and 1-mm mesh edge lengths for 4 mm joint space, sensitivity testing demonstrated best results for 0.5 mm edge length, and repeatable/reliable measurements yielded low coefficient of variation and high intraclass correlation coefficient. These findings support the use of correspondence model-based approaches for robust and accurate analysis of joint measurements related to anatomical features. This method addresses limitations in traditional techniques by incorporating shape variability, providing a practical tool for assessing joint-level disease and deformity. Future work will focus on evaluating the application of this approach in diverse clinical scenarios, including highly deformed joint structures.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665138","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 method incorporating new-grouped SSA with joint ICA and unsupervised clustering for removing multiple artifacts from single-channel EEG.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-18 DOI: 10.1080/10255842.2025.2475462

Murali Krishna Y, Vinay Kumar P

{"title":"A hybrid method incorporating new-grouped SSA with joint ICA and unsupervised clustering for removing multiple artifacts from single-channel EEG.","authors":"Murali Krishna Y, Vinay Kumar P","doi":"10.1080/10255842.2025.2475462","DOIUrl":"https://doi.org/10.1080/10255842.2025.2475462","url":null,"abstract":"Electroencephalogram (EEG) signals collected through ambulatory systems are frequently marred by a medley of disturbances, including electrooculogram (EOG), Motion Artifacts (MA), Electrical Shift and Linear Trend (ESLT), and Electromyography (EMG) artifacts. These artifacts considerably impede the precision of subsequent EEG analysis in practical applications. To date, various approaches have been devised, integrating decomposition methods and Blind Source Separation techniques, to address single or multiple artifacts. However, only a limited number of techniques have been developed for the simultaneous removal of low and high-frequency multiple artifacts from single-channel EEG recordings. It is worth noting that improperly denoised EEG signals can lead to misdiagnosis. In this work, we introduce a novel approach that leverages a new grouped Singular Spectrum Analysis (SSA) technique along with unsupervised k-means clustering controlled Blind Source Separation (BSS) to tackle the simultaneous removal of diverse artifacts from single-channel EEG data. Notably, our method operates without relying on statistical thresholds, thereby enhancing automation in the artifact removal process. The effectiveness of the proposed algorithm is validated using both synthesized and real-world EEG databases, and its performance is evaluated based on metrics such as <math><mrow><mo>Δ</mo></mrow></math> SNR, <math><mrow><mi>η</mi></mrow><mtext>,</mtext></math> and RRMSE.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659289","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