Computer Methods in Biomechanics and Biomedical Engineering最新文献_第8页

Identification of drug use degree by integrating multi-modal features with dual-input deep learning method. 利用双输入深度学习方法整合多模态特征识别吸毒程度。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-28 DOI: 10.1080/10255842.2024.2417206

Yuxing Zhou, Xuelin Gu, Zhen Wang, Xiaoou Li

引用次数: 0

Use of machine learning methods in diagnosis of carpal tunnel syndrome. 使用机器学习方法诊断腕管综合征。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-28 DOI: 10.1080/10255842.2024.2417200

Erol Öten, Nilüfer Aygün Bilecik, Levent Uğur

引用次数: 0

Understanding cervical spine instability: a finite element study on atypical hangman's fractures. 了解颈椎的不稳定性：对非典型刽子手骨折的有限元研究。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-28 DOI: 10.1080/10255842.2024.2421177

Dávid Danka, Imre Bojtár

引用次数: 0

The mouthguard for sports is capable of protecting the implant/crown complex when there is a frontal impact? Responding with finite element analisys. 运动用护齿器能否在正面撞击时保护种植体/冠复合体？用有限元分析来回答。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-26 DOI: 10.1080/10255842.2024.2417201

Victor Paes Dias Gonçalves, Eduardo Henrique Silva Wolf, Laura Domingues Habbema, Neide Pena Coto, Fabiano Capato de Brito, Eduardo Cláudio Lopes de Chaves E Mello Dias

引用次数: 0

An optimized method for dose-effect prediction of traditional Chinese medicine based on 1D-ResCNN-PLS. 基于1D-ResCNN-PLS的中药剂量效应预测优化方法

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-24 DOI: 10.1080/10255842.2024.2417203

Wangping Xiong, Jiasong Pan, Zhaoyang Liu, Jianqiang Du, Yimin Zhu, Jigen Luo, Ming Yang, Xian Zhou

引用次数: 0

Multimodal human computer interaction of wheelchairs supporting lower limb active rehabilitation. 支持下肢主动康复的轮椅的多模式人机交互。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-24 DOI: 10.1080/10255842.2024.2417204

Jie Hong, Miao Cai, Xiansheng Qin

引用次数: 0

Multi-source domain transfer network based on subdomain adaptation and minimum class confusion for EEG emotion recognition. 基于子域适应和最小类混淆的多源域转移网络用于脑电图情感识别。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-21 DOI: 10.1080/10255842.2024.2417212

Lei Zhu, Mengxuan Xu, Aiai Huang, Jianhai Zhang, Xufei Tan

{"title":"Multi-source domain transfer network based on subdomain adaptation and minimum class confusion for EEG emotion recognition.","authors":"Lei Zhu, Mengxuan Xu, Aiai Huang, Jianhai Zhang, Xufei Tan","doi":"10.1080/10255842.2024.2417212","DOIUrl":"https://doi.org/10.1080/10255842.2024.2417212","url":null,"abstract":"Electroencephalogram (EEG) signals, which objectively reflect the state of the brain, are widely favored in emotion recognition research. However, the presence of cross-session and cross-subject variation in EEG signals has hindered the practical implementation of EEG-based emotion recognition technologies. In this article, we propose a multi-source domain transfer method based on subdomain adaptation and minimum class confusion (MS-SAMCC) in response to the addressed issue. First, we introduce the mix-up data augmentation technique to generate augmented samples. Next, we propose a minimum class confusion subdomain adaptation method (MCCSA) as a sub-module of the multi-source domain adaptation module. This approach enables global alignment between each source domain and the target domain, while also achieving alignment among individual subdomains within them. Additionally, we employ minimum class confusion (MCC) as a regularizer for this sub-module. We performed experiments on SEED, SEED IV, and FACED datasets. In the cross-subject experiments, our method achieved mean classification accuracies of 87.14% on SEED, 63.24% on SEED IV, and 42.07% on FACED. In the cross-session experiments, our approach obtained average classification accuracies of 94.20% on SEED and 71.66% on SEED IV. These results demonstrate that the MS-SAMCC approach proposed in this study can effectively address EEG-based emotion recognition tasks.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479841","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 analysis of different types of balloon dilatation catheters for the treatment of intracranial arterial stenosis. 用于治疗颅内动脉狭窄的不同类型球囊扩张导管的模拟分析。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-20 DOI: 10.1080/10255842.2024.2417207

Jiaping Huang, Yuan Yao, Haipo Cui

引用次数: 0

Post-Stroke Dysarthria Voice Recognition based on Fusion Feature MSA and 1D. 基于 MSA 和 1D 融合特征的中风后构音障碍语音识别。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-18 DOI: 10.1080/10255842.2024.2410228

Ye Wujian, Zheng Yingcong, Chen Yuehai, Liu Yijun, Mou Zhiwei

{"title":"Post-Stroke Dysarthria Voice Recognition based on Fusion Feature MSA and 1D.","authors":"Ye Wujian, Zheng Yingcong, Chen Yuehai, Liu Yijun, Mou Zhiwei","doi":"10.1080/10255842.2024.2410228","DOIUrl":"https://doi.org/10.1080/10255842.2024.2410228","url":null,"abstract":"Post-stroke Dysarthria (PSD) is one of the common sequelae of stroke. PSD can harm patients' quality of life and, in severe cases, be life-threatening. Most of the existing methods use frequency domain features to recognize the pathological voice, which makes it hard to completely represent the characteristics of pathological voice. Although some results have been achieved, there is still a long way to go for practical applications. Therefore, an improved deep learning-based model is proposed to classify between the pathological voice and the normal voice, using a novel fusion feature (MSA) and an improved 1D ResNet network hybrid bi-directional LSTM with dilated convolution (named 1D DRN-biLSTM). The experimental results show that our fusion features bring greater improvement in pathological speech recognition than the method that only analyzes the MFCC features, and can better synthesize the hidden features that characterize pathological speech. In terms of model structure, the introduction of dilated convolution and LSTM can further improve the performance of the 1D Resnet network, compared to ordinary networks such as CNN and LSTM. The accuracy of this method reaches 82.41% and 100% at the syllable level and speaker level, respectively. Our scheme outperforms other existing methods in terms of feature learning capability and recognition rate, and will help to play an important role in the assessment and diagnosis of PSD in China.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-11"},"PeriodicalIF":1.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479842","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

Validation of patient-specific flatfoot models on finite element analysis. 通过有限元分析验证患者专用扁平足模型。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-17 DOI: 10.1080/10255842.2024.2417228

Yumiko Kobayashi, Kazuya Ikoma, Masahiro Maki, Kan Imai, Masamitsu Kido, Naoki Okubo, Yasutaka Sotozono, Zhongkui Wang, Shinichi Hirai, Masaki Tanaka, Kenji Takahashi

引用次数: 0