Biomedizinische Technik. Biomedical engineering最新文献_第5页

Biomechanical comparison of three fixation strategies for radial head fractures: a biomechanical study. 桡骨小头骨折三种固定策略的生物力学比较：生物力学研究。

Biomedizinische Technik. Biomedical engineering Pub Date : 2023-10-27 Print Date: 2024-04-25 DOI: 10.1515/bmt-2023-0107

Yao-Tung Tsai, Kun-Jhih Lin, Jui-Cheng Lin

{"title":"Biomechanical comparison of three fixation strategies for radial head fractures: a biomechanical study.","authors":"Yao-Tung Tsai, Kun-Jhih Lin, Jui-Cheng Lin","doi":"10.1515/bmt-2023-0107","DOIUrl":"10.1515/bmt-2023-0107","url":null,"abstract":"Second-generation headless compression screws (HCSs) are commonly used for the fixation of small bones and articular fractures. However, there is a lack of biomechanical data regarding the application of such screws to radial head fractures. This study evaluated the mechanical properties of the fixation of radial head fractures using a single oblique HCS compared with those obtained using a standard locking radial head plate (LRHP) construct and a double cortical screw (DCS) construct. Radial synbone models were used for biomechanical tests of HCS, LRHP, and DCS constructs. All specimens were first cyclically loaded and then loaded to failure. The stiffness for the LRHP group was significantly higher than that for the other two groups, and that for the HCS group was significantly higher than that for the DCS group. The LRHP group had the greatest strength, followed by the HCS group and then the DCS group. The HCS construct demonstrated greater fixation strength than that of the commonly used cortical screws, although the plate group was the most stable. The present study revealed the feasibility of using a single oblique HCS, which has the advantages of being buried, requiring limited wound exposure, and having relatively easy operation, for treating simple radial head fractures.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":"193-198"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic hierarchical clustering based identification and segmentation of brain tumors in magnetic resonance imaging. 磁共振成像中基于概率层次聚类的脑肿瘤识别和分割。

Biomedizinische Technik. Biomedical engineering Pub Date : 2023-10-25 Print Date: 2024-04-25 DOI: 10.1515/bmt-2021-0313

Ankit Vidyarthi

{"title":"Probabilistic hierarchical clustering based identification and segmentation of brain tumors in magnetic resonance imaging.","authors":"Ankit Vidyarthi","doi":"10.1515/bmt-2021-0313","DOIUrl":"10.1515/bmt-2021-0313","url":null,"abstract":"The automatic segmentation of the abnormality region from the head MRI is a challenging task in the medical science domain. The abnormality in the form of the tumor comprises the uncontrolled growth of the cells. The automatic identification of the affected cells using computerized software systems is demanding in the past several years to provide a second opinion to radiologists. In this paper, a new clustering approach is introduced based on the machine learning aspect that clusters the tumor region from the input MRI using disjoint tree generation followed by tree merging. Further, the proposed algorithm is improved by introducing the theory of joint probabilities and nearest neighbors. Later, the proposed algorithm is automated to find the number of clusters required with its nearest neighbors to do semantic segmentation of the tumor cells. The proposed algorithm provides good semantic segmentation results having the DB index-0.11 and Dunn index-13.18 on the SMS dataset. While the experimentation with BRATS 2015 dataset yields Dice complete=80.5 %, Dice core=73.2 %, and Dice enhanced=62.8 %. The comparative analysis of the proposed approach with benchmark models and algorithms proves the model's significance and its applicability to do semantic segmentation of the tumor cells with the average increment in the accuracy of around ±2.5 % with machine learning algorithms.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":"181-192"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49694963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of audio-guided deep breathing and auditory Go/No-Go task on evaluating its impact on the wellness of young adults: a pilot study. 开发音频引导的深呼吸和听觉Go/No-Go任务，以评估其对年轻人健康的影响：一项试点研究。

Biomedizinische Technik. Biomedical engineering Pub Date : 2023-10-23 Print Date: 2024-04-25 DOI: 10.1515/bmt-2023-0410

Eng Keat Kwa, Soon Keng Cheong, Lin Kooi Ong, Poh Foong Lee

{"title":"Development of audio-guided deep breathing and auditory Go/No-Go task on evaluating its impact on the wellness of young adults: a pilot study.","authors":"Eng Keat Kwa, Soon Keng Cheong, Lin Kooi Ong, Poh Foong Lee","doi":"10.1515/bmt-2023-0410","DOIUrl":"10.1515/bmt-2023-0410","url":null,"abstract":"Objectives: Numerous studies indicate that deep breathing (DB) enhances wellbeing. Multiple deep breathing methods exist, but few employ audio to reach similar results. This study developed audio-guided DB and evaluated its immediate impacts on healthy population via self-created auditory Go/No-Go task, tidal volume changes, and psychological measures.Methods: Audio-guided DB with natural sounds to guide the DB was developed. Meanwhile, audio-based Go/No-Go paradigm with Arduino was built to measure the attention level. Thirty-two healthy young adults (n=32) were recruited. Psychological questionnaires (Rosenberg's Self-Esteem Scale (RSES), Cognitive and Affective Mindfulness Scale-Revised (CAMS-R), Perceived Stress Scale (PSS)), objective measurements with tidal volume and attention level with auditory Go/No-Go task were conducted before and after 5 min of DB.Results: Results showed a significant increment in tidal volume and task reaction time from baseline (p=0.003 and p=0.033, respectively). Significant correlations were acquired between (1) task accuracy with commission error (r=-0.905), (2) CAMS-R with task accuracy (r=-0.425), commission error (r=0.53), omission error (r=0.395) and PSS (r=-0.477), and (3) RSES with task reaction time (r=-0.47), task accuracy (r=-0.362), PSS (r=-0.552) and CAMS-R (r=0.591).Conclusions: This pilot study suggests a link between it and young adults' wellbeing and proposes auditory Go/No-Go task for assessing attention across various groups while maintaining physical and mental wellness.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":"141-150"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of driver drowsiness level using a hybrid learning model based on ECG signals. 使用基于ECG信号的混合学习模型检测驾驶员的嗜睡程度。

Biomedizinische Technik. Biomedical engineering Pub Date : 2023-10-13 Print Date: 2024-04-25 DOI: 10.1515/bmt-2023-0193

Hui Xiong, Yan Yan, Lifei Sun, Jinzhen Liu, Yuqing Han, Yangyang Xu

{"title":"Detection of driver drowsiness level using a hybrid learning model based on ECG signals.","authors":"Hui Xiong, Yan Yan, Lifei Sun, Jinzhen Liu, Yuqing Han, Yangyang Xu","doi":"10.1515/bmt-2023-0193","DOIUrl":"10.1515/bmt-2023-0193","url":null,"abstract":"Objectives: Fatigue has a considerable impact on the driver's vehicle and even the driver's own operating ability.Methods: An intelligent algorithm is proposed for the problem that it is difficult to classify the degree of drowsiness generated by the driver during the driving process. By studying the driver's electrocardiogram (ECG) during driving, two models were established to jointly classify the ECG signals as awake, stress, and fatigue or drowsiness states for drowsiness levels. Firstly, the deep learning method was used to establish the model_1 to predict the drowsiness of the original ECG, and model_2 was developed using the combination of principal component analysis (PCA) and weighted K-nearest neighbor (WKNN) algorithm to classify the heart rate variability characteristics. Then, the drowsiness prediction results of the two models were weighted according to certain rules, and the hybrid learning model combining dilated convolution and bidirectional long short-term memory network with PCA and WKNN algorithm was established, and the mixed model was denoted as DiCNN-BiLSTM and PCA-WKNN (DBPW). Finally, the validity of the DBPW model was verified by simulation of the public database.Results: The experimental results show that the average accuracy, sensitivity and F1 score of the test model in the dataset containing multiple drivers are 98.79, 98.81, and 98.79 % respectively, and the recognition accuracy for drowsiness or drowsiness state is 99.33 %.Conclusions: Using the proposed algorithm, it is possible to identify driver anomalies and provide new ideas for the development of intelligent vehicles.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":"151-165"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41223509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A portable household detection system based on the combination of bidirectional LSTM and residual block for automatical arrhythmia detection. 一种基于双向LSTM和残差块相结合的便携式家庭心律失常自动检测系统。

Biomedizinische Technik. Biomedical engineering Pub Date : 2023-09-29 Print Date: 2024-04-25 DOI: 10.1515/bmt-2021-0146

Zeqiong Huang, Shaohua Yang, Qinhong Zou, Xuliang Gao, Bin Chen

{"title":"A portable household detection system based on the combination of bidirectional LSTM and residual block for automatical arrhythmia detection.","authors":"Zeqiong Huang, Shaohua Yang, Qinhong Zou, Xuliang Gao, Bin Chen","doi":"10.1515/bmt-2021-0146","DOIUrl":"10.1515/bmt-2021-0146","url":null,"abstract":"Objectives: Arrhythmia is an important component of cardiovascular disease, and electrocardiogram (ECG) is a method to detect arrhythmia. Arrhythmia detection is often paroxysmal, and ECG signal analysis is time-consuming and expensive. We propose a model and device for convenient monitoring of arrhythmia at any time.Methods: This work proposes a model combining residual block and bidirectional long-term short-term memory network (BiLSTM) to detect and classify ECG signals. Residual blocks can extract deep features and avoid performance degradation caused by convolutional networks. Combined with the feature of BiLSTM to strengthen the connection relationship of the local window, it can achieve a better classification and prediction effect.Results: Model optimization experiments were performed on the MIT-BIH Atrial Fibrillation Database (AFDB) and MIT-BIH Arrhythmia Database (MITDB). The accuracy simulation results on both long and short signal was higher than 99 %. To further demonstrate the applicability of the model, validation experiments were conducted on MIT-BIH Normal Sinus Rhythm Database (NSRDB) and the Long-Term AF Database (LTAFDB) datasets, and the related recognition accuracy were 99.830 and 91.252 %, respectively. Additionally, we proposed a portable household detection system including an ECG and a blood pressure detection module. The detection accuracy was higher than 98 % using the collected data as testing set.Conclusions: Hence, we thought our system can be used for practical application.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":"167-179"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41143269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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06-1745-B22. 06 - 1745 b22。

Biomedizinische Technik. Biomedical engineering Pub Date : 2021-10-01 DOI: 10.1515/bmt-2021-6050

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05-1500-A16. 05 - 1500系。

Biomedizinische Technik. Biomedical engineering Pub Date : 2021-10-01 DOI: 10.1515/bmt-2021-6016

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05-0915-A1. 05 - 0915 - a1。

Biomedizinische Technik. Biomedical engineering Pub Date : 2021-10-01 DOI: 10.1515/bmt-2021-6001

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06-1745-B21. 06 - 1745 b21。

Biomedizinische Technik. Biomedical engineering Pub Date : 2021-10-01 DOI: 10.1515/bmt-2021-6049

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05-1700-P5. 05 - 1700 - p5。

Biomedizinische Technik. Biomedical engineering Pub Date : 2021-10-01 DOI: 10.1515/bmt-2021-6022

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