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Compressed sensing study for the sEMG data of SCI survivors. 脊髓损伤幸存者表面肌电信号的压缩感知研究。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-08 DOI: 10.1080/10255842.2025.2554254
Zongxian Feng, Beining Cui, Fan He, Zhehan Wu, Tianle Cheng, Haoxiang Zhang
{"title":"Compressed sensing study for the sEMG data of SCI survivors.","authors":"Zongxian Feng, Beining Cui, Fan He, Zhehan Wu, Tianle Cheng, Haoxiang Zhang","doi":"10.1080/10255842.2025.2554254","DOIUrl":"https://doi.org/10.1080/10255842.2025.2554254","url":null,"abstract":"<p><p>Surface electromyography (sEMG) holds great potential in walking function evaluation. Compressed sensing (CS) leverages the sparsity of signals to decrease the number of samples required. In this study, a sEMG CS algorithm for spinal cord injury (SCI) patients based on regularized orthogonal matching pursuit (ROMP) was introduced. It was used to reconstruct multiple sEMG signals collected from SCI subjects. Its performance was compared with orthogonal matching pursuit (OMP). The impact of diverse measurement matrices on reconstruction accuracy was also evaluated. Results indicates that the combination of ROMP with a binary permuted block diagonal (BPBD) matrix outperforms the conventional OMP algorithm.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-9"},"PeriodicalIF":1.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016538","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
An automated coronary artery disease identification using photoplethysmography signals with deep feature representations. 利用具有深度特征表征的光容积脉搏波信号自动识别冠状动脉疾病。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-08 DOI: 10.1080/10255842.2025.2558045
Jenn-Kaie Lain, Shing-Yu Chen, Chen-Wei Lee, Tin-Kwang Lin
{"title":"An automated coronary artery disease identification using photoplethysmography signals with deep feature representations.","authors":"Jenn-Kaie Lain, Shing-Yu Chen, Chen-Wei Lee, Tin-Kwang Lin","doi":"10.1080/10255842.2025.2558045","DOIUrl":"https://doi.org/10.1080/10255842.2025.2558045","url":null,"abstract":"<p><p>This study explores deep feature representations from photoplethysmography (PPG) signals for coronary artery disease (CAD) identification in 80 participants (40 with CAD). Finger PPG signals were processed using multilayer perceptron (MLP) and convolutional neural network (CNN) autoencoders, with performance assessed via 5-fold cross-validation. The CNN autoencoder model achieved the best results (recall 96.67%, precision 96.71%, accuracy 96.11%), outperforming MLP features and time-series imaging methods (<90%). These findings highlight the efficacy of CNN-extracted PPG features, offering a low-cost, minimally pre-processed, and portable approach for CAD diagnosis confirmed by cardiac catheterization.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016576","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
Research based on EEG for addiction level assessment methods and parietal/occipital lobes brain function analysis. 基于脑电图的成瘾水平评估方法及顶叶/枕叶脑功能分析研究。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-06 DOI: 10.1080/10255842.2025.2551014
Wenrui Huang, Xuelin Gu, Xiaoou Li
{"title":"Research based on EEG for addiction level assessment methods and parietal/occipital lobes brain function analysis.","authors":"Wenrui Huang, Xuelin Gu, Xiaoou Li","doi":"10.1080/10255842.2025.2551014","DOIUrl":"https://doi.org/10.1080/10255842.2025.2551014","url":null,"abstract":"<p><p>The methamphetamine use disorder (MUD) has emerged as a global public health concern. This article proposes an assessment method that combines electroencephalography (EEG)-based deep learning, visualization and time domain and frequency domain analysis, aiming to ensure accuracy while identifying corresponding brain channels and improving assessment efficiency. The collected EEG data were classified correctly using a enhanced compact convolutional neural network, namely ECCN-Net. The classification results were validated using time domain and frequency domain analysis and Class Activation Mapping (CAM) visualization. The accuracy of the PO3 channel is the highest, reaching 85.15%. It is also discovered that MUD individuals have relatively higher relative power in the delta band.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.6,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006792","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 effect of automatic brake system on the response of the lower limbs based on active muscle force. 基于主动肌力的自动制动系统对下肢反应的影响。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-06 DOI: 10.1080/10255842.2025.2556002
Kang Lei, Binhai Xia, Yuanchang Huang, Haiyun Zhu, Fan Li
{"title":"The effect of automatic brake system on the response of the lower limbs based on active muscle force.","authors":"Kang Lei, Binhai Xia, Yuanchang Huang, Haiyun Zhu, Fan Li","doi":"10.1080/10255842.2025.2556002","DOIUrl":"https://doi.org/10.1080/10255842.2025.2556002","url":null,"abstract":"<p><p>This article employs a finite element model integrated with the Hybrid III dummy to investigate how automatic braking and active muscle forces influence lower-limb injuries in frontal collisions. Prolonged braking can increase the tibial index, indicating more severe injury to the lower leg. Braking mitigated thigh injury at 50 km/h but exacerbated it at 40 km/h. Active muscle activation increased the femoral axial force and tibial index but decreased tibial and fibular peak stresses by approximately 0.006-0.009 GPa. These findings highlight the complex role of braking and muscle activation in lower-limb injuries and inform the development of advanced safety system designs.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.6,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006763","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
CDLR-net: a ECG classification network based on deep residual shrinkage networks and LSTM. CDLR-net：基于深度残差收缩网络和LSTM的心电分类网络。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-06 DOI: 10.1080/10255842.2025.2554260
Zhanhang Qiu, Suigu Tang, Huazhu Liu, Xiaofang Zhao, Junhui Lin
{"title":"CDLR-net: a ECG classification network based on deep residual shrinkage networks and LSTM.","authors":"Zhanhang Qiu, Suigu Tang, Huazhu Liu, Xiaofang Zhao, Junhui Lin","doi":"10.1080/10255842.2025.2554260","DOIUrl":"https://doi.org/10.1080/10255842.2025.2554260","url":null,"abstract":"<p><p>Many traditional classification networks directly use the limb two-lead signal (MLII) ECG signals as input for training. However, this method suffers from reduced accuracy when ECG features are not obvious, especially for premature heartbeats. To solve the issue, this paper proposed a novel network, namely CDLR-Net, that combines a Deep Residual Shrinkage Network (DRSN) with a Long Short-Term Memory (LSTM). The model combines MLII lead data with RR interval features. ECG signals are first denoised by wavelet decomposition, after which pre-RR, post-RR, local-10 average RR, and overall average RR intervals are extracted from R-wave localization for each heartbeat. Incorporating RR interval information improves classification accuracy. Finally, the classification is achieved through proposed method. Experiments on the MIT-BIH database under inter-patient and intra-patient schemes achieved 97% and 99% accuracy, respectively, demonstrating the effectiveness of the proposed method.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.6,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145008540","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
Heart disease risk prediction based on deep learning multi-scale convolutional enhanced Swin Transformer model. 基于深度学习多尺度卷积增强Swin Transformer模型的心脏病风险预测。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-05 DOI: 10.1080/10255842.2025.2556004
Shengli Li, Zhangyi Shen, Qiqi Song, Yihan Gong, Kaizhong Zuo, Peng Hu, Wenjie Li
{"title":"Heart disease risk prediction based on deep learning multi-scale convolutional enhanced Swin Transformer model.","authors":"Shengli Li, Zhangyi Shen, Qiqi Song, Yihan Gong, Kaizhong Zuo, Peng Hu, Wenjie Li","doi":"10.1080/10255842.2025.2556004","DOIUrl":"https://doi.org/10.1080/10255842.2025.2556004","url":null,"abstract":"<p><p>Heart disease is a leading global cause of death, making early prediction critical. This study proposes a multi-scale convolution-enhanced Swin Transformer (MSCST) model for heart disease risk assessment. The model employs a multi-branch convolutional network with channel attention to extract and optimize multi-scale features. These features are processed by a Swin Transformer module to integrate global and local information via self-attention. SHAP analysis is incorporated to enhance interpretability. Evaluated on the Cleveland Heart Disease dataset, MSCST achieved 89.42% accuracy and an AUC of 0.8908, outperforming both traditional machine learning and existing deep learning methods.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-9"},"PeriodicalIF":1.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006795","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 the reflection at the soft tissue-bone interface on propagating characteristics of radial extracorporeal shock wave in soft tissue. 软组织-骨界面反射对径向体外冲击波在软组织内传播特性的影响。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-05 DOI: 10.1080/10255842.2025.2554255
Qiong Wu, Fan Fan, Liansheng Xu, Fei Shen, Li Wang, Fengji Li, Youguo Hao, Haijun Niu
{"title":"Effect of the reflection at the soft tissue-bone interface on propagating characteristics of radial extracorporeal shock wave in soft tissue.","authors":"Qiong Wu, Fan Fan, Liansheng Xu, Fei Shen, Li Wang, Fengji Li, Youguo Hao, Haijun Niu","doi":"10.1080/10255842.2025.2554255","DOIUrl":"https://doi.org/10.1080/10255842.2025.2554255","url":null,"abstract":"<p><p>This study aims to investigate the effect of reflection at the soft tissue-bone interface on shock wave propagation within soft tissue using finite element methods. Results showed that reflection caused obvious differences in the propagation process and attenuation characteristics of shock waves. The energy flux density (EFD) at the same target was proportional to the impact pressure. Changes in EFD at the target could be estimated using the impact pressure and the distance between the soft tissue-bone interface and the target (RMSE < 0.01). These results provide a reference for evaluating rESWT equipment and optimizing clinical treatment protocols.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-11"},"PeriodicalIF":1.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006801","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
Design and motion control analysis of a hybrid-powered ankle rehabilitation robot. 混合动力踝关节康复机器人设计与运动控制分析。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-04 DOI: 10.1080/10255842.2025.2556304
Xiangfeng Zeng, Wenxing Liao
{"title":"Design and motion control analysis of a hybrid-powered ankle rehabilitation robot.","authors":"Xiangfeng Zeng, Wenxing Liao","doi":"10.1080/10255842.2025.2556304","DOIUrl":"https://doi.org/10.1080/10255842.2025.2556304","url":null,"abstract":"<p><p>This study presents a novel hybrid-powered ankle robot actuated from above (ARAA) designed to improve the smoothness and control of multiaxial movements in robot-assisted ankle rehabilitation. Addressing the limitations of existing systems, which often lack precise trajectory tracking and consistent force application, the proposed robot integrates pneumatic muscles for actuation along the X-axis and Y-axis, with a servo motor driving motion in the Z-axis. A PID-based posture controller is implemented to ensure accurate control during training, while a reconfigurable mechanism allows adjustment of motion parameters to accommodate individual physiological differences. Preliminary testing with a healthy participant demonstrated successful execution of both single-axis and multiaxial training protocols. The system achieved low trajectory tracking errors, with Root Mean Square Deviation (RMSD) and Normalized Root Mean Square Deviation (NRMSD) values of 0.0164 rad and 2.73 along the X-axis, 0.007 rad and 1.9 along the Y-axis, and 0.0012 rad and 0.31 along the Z-axis, indicating alignment with the requirements for effective rehabilitation. Force application by the pneumatic muscles closely followed the predefined trajectory, confirming high fidelity in force control. The results show that the hybrid-powered ARAA effectively meets the demands of controlled ankle training, offering enhanced precision and adaptability. This work contributes to advancing ankle rehabilitation technology by providing a more efficient and customizable solution for patient recovery.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.6,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994366","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
Automatic epileptic seizure detection method based on spatio-temporal feature fusion. 基于时空特征融合的癫痫发作自动检测方法。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-03 DOI: 10.1080/10255842.2025.2551845
Xia Zhang, Caini Yan, Yali Ren, Zhang Jianrui
{"title":"Automatic epileptic seizure detection method based on spatio-temporal feature fusion.","authors":"Xia Zhang, Caini Yan, Yali Ren, Zhang Jianrui","doi":"10.1080/10255842.2025.2551845","DOIUrl":"https://doi.org/10.1080/10255842.2025.2551845","url":null,"abstract":"<p><p>This paper proposes a spatiotemporal feature fusion method for automatic epileptic seizure detection, integrating Common Spatial Pattern (CSP) and Least Squares Support Vector Machine (LSSVM). First, it reconstructs electroencephalogram (EEG) noise using Ensemble Empirical Mode Decomposition (EEMD), then decomposes the original EEG signals using improved EEMD (IEEMD). Next, features are extracted from temporal and spatial dimensions to form a feature set. The classification process adopts a novel dual-classification mode based on LSSVM ultimately achieving high-performance automatic recognition of normal, seizure, and interictal EEG signals. Validated on Bonn and CHB-MIT EEG datasets, the IEEMD algorithm achieves 99.57% ± 0.02 accuracy on Bonn and 96.43% overall accuracy on CHB-MIT. Results show IEEMD and spatiotemporal features effectively address low interictal-ictal recognition rates in existing studies, offering a reliable means for epileptic seizure prediction.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977298","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
Research on gait recognition of lower limb exoskeleton robot based on sEMG&IMU feature fusion. 基于sEMG&IMU特征融合的下肢外骨骼机器人步态识别研究。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-02 DOI: 10.1080/10255842.2025.2554257
Chikun Gong, Bingsheng Wei, Yong Huang, Lipeng Yuan, Yuqing Hu, Yufeng Xiong
{"title":"Research on gait recognition of lower limb exoskeleton robot based on sEMG&IMU feature fusion.","authors":"Chikun Gong, Bingsheng Wei, Yong Huang, Lipeng Yuan, Yuqing Hu, Yufeng Xiong","doi":"10.1080/10255842.2025.2554257","DOIUrl":"https://doi.org/10.1080/10255842.2025.2554257","url":null,"abstract":"<p><p>Aiming at the problems of low accuracy and poor robustness in gait recognition of lower extremity exoskeleton robots in human-computer interaction, a depth residual contraction network recognition method based on the fusion of surface electrosemg (sEMG) and inertial measurement unit (IMU) signals was proposed. Firstly, a new energy kernel feature extraction method was used to extract sEMG signals. Based on the sEMG oscillator model, the sEMG energy kernel phase diagram was converted to gray level map by matrix counting method. Secondly, the IMU signal is denoised and processed graphically. Then, deep residual contraction network (DRSN) was used to recognize sEMG and IMU signals in lower limbs. Finally, experimental hardware was deployed in the wearer's lower limbs, and the algorithm was used to conduct offline and online recognition experiments of three common gaits. Different comparative experiments show that the attention mechanism of DRSN network can significantly improve the classification effect, and the recognition accuracy is improved by 10%-20% compared with single source signal and other feature extraction methods, and finally the recognition accuracy reaches more than 90% through online experiments. The multi-feature fusion network based on energy kernel feature extraction is time-efficient, high-accuracy and robust, and has real-world application value in the field of exoskeleton robotics.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977362","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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