Biomedical Engineering / Biomedizinische Technik最新文献_第7页

Stratification of risk of atherosclerotic plaque using Hu's moment invariants of segmented ultrasonic images. 利用分段超声图像的Hu不变矩对动脉粥样硬化斑块风险进行分层。

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-07-15 Print Date: 2022-10-26 DOI: 10.1515/bmt-2021-0044

Smitha Balakrishnan, Paul K Joseph

{"title":"Stratification of risk of atherosclerotic plaque using Hu's moment invariants of segmented ultrasonic images.","authors":"Smitha Balakrishnan, Paul K Joseph","doi":"10.1515/bmt-2021-0044","DOIUrl":"https://doi.org/10.1515/bmt-2021-0044","url":null,"abstract":"Myocardial infarction is one of the major life-threatening diseases. The cause is atherosclerosis i.e. the occlusion of the coronary artery by deposition of plaque on its walls. The severity of plaque deposition in the artery depends on the characteristics of the plaque. Hence, the classification of the type of plaque is crucial for assessing the risk of atherosclerosis and predicting the chances of myocardial infarction. This paper proposes prediction of atherosclerotic risk by non-invasive ultrasound image segmentation and textural feature extraction. The intima-media complex is segmented using a snakes-based segmentation algorithm on the arterial wall in the ultrasound images. Then, the plaque is extracted from the segmented intima-media complex. The features of the plaque are obtained by computing Hu's moment invariants. Visual pattern recognition independent of position, size, orientation and parallel projection could be done using these moment invariants. For the classification of the features of the plaque, an SVM classifier is used. The performance shows improvement in accuracy using lesser number of features than previous works. The reduction in feature size is achieved by incorporating segmentation in the pre-processing stage. Tenfold cross-validation protocol is used for training and testing the classifier. An accuracy of 97.9% is obtained with only two features. This proposed technique could work as an adjunct tool in quick decision-making for cardiologists and radiologists. The segmentation step introduced in the preprocessing stage improved the feature extraction technique. An improvement in performance is achieved with much less number of features.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"67 5","pages":"391-402"},"PeriodicalIF":1.7,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40534458","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: 3

Automatic landmark identification for surgical 3d-navigation - A proposed method for marker-free dental surgical navigation systems. 用于外科手术3d导航的自动地标识别-一种用于无标记牙科手术导航系统的建议方法。

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-07-04 Print Date: 2022-10-26 DOI: 10.1515/bmt-2021-0307

Micha Bischofberger, Stephan Böhringer, Erik Schkommodau

{"title":"Automatic landmark identification for surgical 3d-navigation - A proposed method for marker-free dental surgical navigation systems.","authors":"Micha Bischofberger, Stephan Böhringer, Erik Schkommodau","doi":"10.1515/bmt-2021-0307","DOIUrl":"https://doi.org/10.1515/bmt-2021-0307","url":null,"abstract":"This paper proposes a conceptual method to calculate the pose of a stereo-vision camera relative to an artificial mandible without additional markers. The general method for marker-free navigation has four steps: 1) parallel image acquisition by a stereo-vision camera, 2) automatic identification of 2d point pairs (landmark pairs) in a left and a right image, 3) calculation of related 3d points in the joint camera coordinate system and 4) matching of 3d points generated to a preoperative 3d model (i.e., CT data based). To identify and compare landmarks in the acquired stereo images, well-known algorithms for landmark detection, description and matching were compared within the developed approach. Finally, the BRISK algorithm (Leutenegger S, Chli M, Siegwart RY. BRISK: Binary Robust invariant scalable keypoints. Proceedings of the IEEE International Conference on Computer Vision; 2011: 2548-2555) was used. The proposed method was implemented in MATLAB® and validated in vitro with one artificial mandible. The accuracy evaluation of the camera positions calculated resulted in an average deviation error of 1.45 mm ± 0.76 mm to the real camera displacement. This value was calculated using only stereo images with over 100 reconstructed landmark pairs each. This provides the basis for marker-free navigation.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"67 5","pages":"411-417"},"PeriodicalIF":1.7,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40479492","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

Analysis of pilots' EEG map in take-off and landing tasks. 起降任务中飞行员脑电图分析。

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-06-28 Print Date: 2022-10-26 DOI: 10.1515/bmt-2021-0418

Li Ji, Chen Zhang, Haiwei Li, Ningning Zhang, Peng Zheng, Changhao Guo, Yong Zhang, Xiaoyu Tang

引用次数: 0

Automatic sleep scoring with LSTM networks: impact of time granularity and input signals LSTM网络的自动睡眠评分:时间粒度和输入信号的影响

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-06-06 DOI: 10.1515/bmt-2021-0408

Alexandra-Maria Tăuțan, A. C. Rossi, B. Ionescu

{"title":"Automatic sleep scoring with LSTM networks: impact of time granularity and input signals","authors":"Alexandra-Maria Tăuțan, A. C. Rossi, B. Ionescu","doi":"10.1515/bmt-2021-0408","DOIUrl":"https://doi.org/10.1515/bmt-2021-0408","url":null,"abstract":"Abstract Supervised automatic sleep scoring algorithms are usually trained using sleep stage labels manually annotated on 30 s epochs of PSG data. In this study, we investigate the impact of using shorter epochs with various PSG input signals for training and testing a Long Short Term Memory (LSTM) neural network. An LSTM model is evaluated on the provided 30 s epoch sleep stage labels from a publicly available dataset, as well as on 10 s subdivisions. Additionally, three independent scorers re-labeled a subset of the dataset on shorter time windows. The automatic sleep scoring experiments were repeated on the re-annotated subset.The highest performance is achieved on features extracted from 30 s epochs of a single channel frontal EEG. The resulting accuracy, precision and recall were of 92.22%, 67.58% and 66.00% respectively. When using a shorter epoch as input, the performance decreased by approximately 20%. Re-annotating a subset of the dataset on shorter time epochs did not improve the results and further altered the sleep stage detection performance. Our results show that our feature-based LSTM classification algorithm performs better on 30 s PSG epochs when compared to 10 s epochs used as input. Future work could be oriented to determining whether varying the epoch size improves classification outcomes for different types of classification algorithms.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"53 1","pages":"267 - 281"},"PeriodicalIF":1.7,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87029110","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: 1

Layer recurrent neural network-based diagnosis of Parkinson’s disease using voice features 基于层递归神经网络的帕金森病语音特征诊断

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-06-03 DOI: 10.1515/bmt-2022-0022

Z. Senturk

{"title":"Layer recurrent neural network-based diagnosis of Parkinson’s disease using voice features","authors":"Z. Senturk","doi":"10.1515/bmt-2022-0022","DOIUrl":"https://doi.org/10.1515/bmt-2022-0022","url":null,"abstract":"Abstract Parkinson’s disease (PD), a slow-progressing neurological disease, affects a large percentage of the world’s elderly population, and this population is expected to grow over the next decade. As a result, early detection is crucial for community health and the future of the globe in order to take proper safeguards and have a less arduous treatment procedure. Recent research has begun to focus on the motor system deficits caused by PD. Because practically most of the PD patients suffer from voice abnormalities, researchers working on automated diagnostic systems investigate vocal impairments. In this paper, we undertake extensive experiments with features extracted from voice signals. We propose a layer Recurrent Neural Network (RNN) based diagnosis for PD. To prove the efficiency of the model, different network models are compared. To the best of our knowledge, several neural network topologies, namely RNN, Cascade Forward Neural Networks (CFNN), and Feed Forward Neural Networks (FFNN), are used and compared for voice-based PD detection for the first time. In addition, the impacts of data normalization and feature selection (FS) are thoroughly examined. The findings reveal that normalization increases classifier performance and Laplacian-based FS outperforms. The proposed RNN model with 300 voice features achieves 99.74% accuracy.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"12 1","pages":"249 - 266"},"PeriodicalIF":1.7,"publicationDate":"2022-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75749149","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: 3

Frontmatter

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-06-01 DOI: 10.1515/bmt-2022-frontmatter3

引用次数: 0

Fetal phonocardiogram signals denoising using improved complete ensemble (EMD) with adaptive noise and optimal thresholding of wavelet coefficients 利用改进的全系综(EMD)自适应噪声和最佳小波系数阈值对胎儿心音信号进行降噪

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-06-01 DOI: 10.1515/bmt-2022-0006

Fethi Cheikh, Nasser Edinne Benhassine, S. Sbaa

{"title":"Fetal phonocardiogram signals denoising using improved complete ensemble (EMD) with adaptive noise and optimal thresholding of wavelet coefficients","authors":"Fethi Cheikh, Nasser Edinne Benhassine, S. Sbaa","doi":"10.1515/bmt-2022-0006","DOIUrl":"https://doi.org/10.1515/bmt-2022-0006","url":null,"abstract":"Abstract Although fetal phonocardiogram (fPCG) signals have become a good indicator for discovered heart disease, they may be contaminated by various noises that reduce the signals quality and the final diagnosis decision. Moreover, the noise may cause the risk of the data to misunderstand the heart signal and to misinterpret it. The main objective of this paper is to effectively remove noise from the fPCG signal to make it clinically feasible. So, we proposed a novel noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), wavelet threshold and Crow Search Algorithm (CSA). This noise reduction method, named ICEEMDAN-DWT-CSA, has three major advantages. They were, (i) A better suppress of mode mixing and a minimized number of IMFs, (ii) A choice of wavelet corresponding to the study signal proven by the literature and (iii) Selection of the optimal threshold value. Firstly, the noisy fPCG signal is decomposed into Intrinsic Mode Functions (IMFs) by the (ICEEMDAN). Each noisy IMFs were decomposed by the Discrete Wavelet Transform (DWT). Then, the optimal threshold value using the (CSA) technique is selected and the thresholding function is carried out in the detail’s coefficients. Secondly, each denoised (IMFs) is reconstructed by applying the Inverse Discrete Wavelet Transform (IDWT). Finally, all these denoised (IMFs) are combined to get the denoised fPCG signal. The performance of the proposed method has been evaluated by Signal to Noise Ratio (SNR), Mean Square Error (MSE) and the Correlation Coefficient (COR). The experiment gave a better result than some standard methods.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"353 1","pages":"237 - 247"},"PeriodicalIF":1.7,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80079336","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: 2

Non-woven textiles for medical implants: mechanical performances improvement 医用植入物用无纺布纺织品:机械性能改进

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-05-25 DOI: 10.1515/bmt-2022-0017

Amandine Lequeux, B. Mazé, G. Laroche, F. Heim

{"title":"Non-woven textiles for medical implants: mechanical performances improvement","authors":"Amandine Lequeux, B. Mazé, G. Laroche, F. Heim","doi":"10.1515/bmt-2022-0017","DOIUrl":"https://doi.org/10.1515/bmt-2022-0017","url":null,"abstract":"Abstract Non-woven textile has been largely used as medical implant material over the last decades, especially for scaffold manufacturing purpose. This material presents a large surface area-to-volume ratio, which promotes adequate interaction with biological tissues. However, its strength is limited due to the lack of cohesion between the fibers. The goal of the present work was to investigate if a non-woven substrate can be reinforced by embroidery stitching towards strength increase. Non-woven samples were produced from both melt-blowing and electro-spinning techniques, reinforced with a stitching yarn and tested regarding several performances: ultimate tensile strength, burst strength and strength loss after fatigue stress. Several stitching parameters were considered: distance between stitches, number of stitch lines (1, 2 or 3) and line geometry (horizontal H, vertical L, cross X). The performance values obtained after reinforcement were compared with values obtained for control samples. Results bring out that reinforcement can increase the strength by up to 50% for a melt-blown mat and by up to 100% for an electro-spun mat with an X reinforcement pattern. However, after cyclic loading, the reinforcement yarn tends to degrade the ES mat in particular. Moreover, increasing the number of stitches tends to fragilize the mats.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"68 1","pages":"317 - 330"},"PeriodicalIF":1.7,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89365592","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 comparison of different prosthetic materials and posterior implant angles in all-on-4 treatment concept by three-dimensional finite element analysis 采用三维有限元分析比较全on-4治疗概念中不同假体材料和后路种植体角度的生物力学差异

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-05-20 DOI: 10.1515/bmt-2022-0109

Ayhan Gürbüz, Z. Güçlü, Gonca Deste Gökay, R. Durkan

{"title":"Biomechanical comparison of different prosthetic materials and posterior implant angles in all-on-4 treatment concept by three-dimensional finite element analysis","authors":"Ayhan Gürbüz, Z. Güçlü, Gonca Deste Gökay, R. Durkan","doi":"10.1515/bmt-2022-0109","DOIUrl":"https://doi.org/10.1515/bmt-2022-0109","url":null,"abstract":"Abstract The study aimed to evaluate the biomechanical behaviors of different prosthetic materials and posterior implant angles in All-on-4 implant-supported fixed maxillary prostheses with three-dimensional (3D) finite element analysis. The model of complete edentulous maxilla was created using the Rhinoceros and VRMesh Studio programs. Anterior vertical and 17°- and 30°-angled posterior implants were positioned with All-on-4 design. Straigth and angled multi-unit abutments scanned using a 3D scanner. Two different prosthetic superstructures (monolithic zirconia framework and lithium disilicate veneer (ZL) and monolithic zirconia-reinforced lithium silicate (ZLS)) were modeled. Four models designed according to the prosthetic structure and posterior implant angles. Posterior vertical bilateral loading and frontal oblique loading was performed. The principal stresses (bone tissues-Pmax and Pmin) and von Mises equivalent stresses (implant and prosthetic structures) were analyzed. In all models, the highest Pmax stress values were calculated under posterior bilateral loading in cortical bone. The highest von Mises stress levels occured in the posterior implants under posterior bilateral load (260.33 and 219.50 MPa) in the ZL-17 and ZL-30 models, respectively. Under both loads, higher stress levels in prosthetic structures were shown in the ZLS models compared with ZL models. There was no difference between posterior implant angles on stress distribution occurred in implant material and alveolar bone tissue. ZLS and ZL prosthetic structures can be reliably used in maxillary All-on-4 rehabilitation.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"1 1","pages":"307 - 315"},"PeriodicalIF":1.7,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84007664","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

Computer-aided diagnosis system for retinal disorder classification using optical coherence tomography images 基于光学相干断层成像的视网膜疾病分类计算机辅助诊断系统

IF 1.7 4区医学

Biomedical Engineering / Biomedizinische Technik Pub Date : 2022-05-18 DOI: 10.1515/bmt-2021-0330

N. Saleh, Manal Abdel Wahed, A. M. Salaheldin

{"title":"Computer-aided diagnosis system for retinal disorder classification using optical coherence tomography images","authors":"N. Saleh, Manal Abdel Wahed, A. M. Salaheldin","doi":"10.1515/bmt-2021-0330","DOIUrl":"https://doi.org/10.1515/bmt-2021-0330","url":null,"abstract":"Abstract The incidence of vision impairment is rapidly increasing. Diagnosis and classifying retinal abnormalities in ophthalmological applications is a significant challenge. Using Optical Coherence Tomography (OCT), the study aims to develop a computer aided diagnosis system for detecting and classifying retinal disorders. Choroidal neovascularization, diabetic macular edema, drusen, and normal cases are the investigated groups. Both deep learning and machine learning are combined to build the system. The SqueezeNet neural network was modified to extract features. The Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Decision Tree (DT), and Ensemble Model (EM) algorithms were used for disorder classification. The Bayesian optimization technique was also used to determine the best hyperparameters for each model. The model’ performance was evaluated through nine criteria using 12,000 OCT images. The results have demonstrated accuracies of 97.39, 97.47, 96.98, and 95.25% for the SVM, K-NN, DT, and EM, respectively. When results are compared to relevant studies in terms of accuracy and tested samples, they show superior performance. As a result, a novel computer-aided diagnosis system for detecting and classifying retinal diseases has been developed, reducing human error while also saving time.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"27 1","pages":"283 - 294"},"PeriodicalIF":1.7,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80097886","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: 5