2020 Medical Technologies Congress (TIPTEKNO)最新文献

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A Study On Finding The Optimal Time For Automatic Transition To Self-Driving Mode 汽车自动切换至自动驾驶模式的最佳时间选择研究
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299243
F. Nassehi, Başak Erdoğdu, Sena Şişman, Yağmur Sağlam, O. Eroğul
{"title":"A Study On Finding The Optimal Time For Automatic Transition To Self-Driving Mode","authors":"F. Nassehi, Başak Erdoğdu, Sena Şişman, Yağmur Sağlam, O. Eroğul","doi":"10.1109/TIPTEKNO50054.2020.9299243","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299243","url":null,"abstract":"Topic of self-driving mode and transition to this mode is one of the trend topics of biomedical engineering and artificial intelligence studies. Sleeplessness and sleep efficiency to cause inattention in driving and accidents. This study aimed to investigate convenient time to transit self-driving mode respect to number of accidents and sleep efficiency of driver by using Support Vector Machines and K-Nearest neighbors classification algorithms to reduce the accidents. Approximate entropy and Lyapunov exponent for Electroencephalography and dominant frequency, ratio of power of high frequency to low frequency, area under the curve and derivative respiration signals were extracted. This proposal method achieves 93.33% and 100% accuracies to classify drivers and transit car to self-driving mode respect to two criteria.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129228541","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
C# Interface Design for Real-Time Signal Recording Oriented of Bionic Hand Control with Leap Motion and EMG Devices 面向跳跃运动与肌电装置仿生手控实时信号记录的c#接口设计
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299309
A. Kavsaoğlu, ve Burak Bi̇lece, Besimcan Altiyaprak, ve Furkan Böyükçolak
{"title":"C# Interface Design for Real-Time Signal Recording Oriented of Bionic Hand Control with Leap Motion and EMG Devices","authors":"A. Kavsaoğlu, ve Burak Bi̇lece, Besimcan Altiyaprak, ve Furkan Böyükçolak","doi":"10.1109/TIPTEKNO50054.2020.9299309","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299309","url":null,"abstract":"There are people who have a lost limb or have no innate limb. In this study, it is aimed to create a data processing environment to improve the working performance of the prostheses to be developed for people with hand loss. Basically, Leap Motion and EMG devices were used. Simultaneous recording of data obtained with EMG and Leap Motion is provided using Arduino microcontroller and C # Interface design. In addition, a bionic hand control is provided from finger movements obtained with Leap Motion.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"436 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123198974","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
Diagnosis of COVID-19 with a Deep Learning Approach on Chest CT Slices 基于胸部CT片深度学习的COVID-19诊断
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299266
Fatma Muberra Yener, A. B. Oktay
{"title":"Diagnosis of COVID-19 with a Deep Learning Approach on Chest CT Slices","authors":"Fatma Muberra Yener, A. B. Oktay","doi":"10.1109/TIPTEKNO50054.2020.9299266","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299266","url":null,"abstract":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first broke out in Wuhan, China and COVID-19 disease spread throughout the world by its highly contagious nature. High death numbers have caused a massive panic across the globe. Fast and early diagnosis is the key for preventing the virus from spreading. Besides PCR test, computed tomography (CT) of lungs is also used for diagnosis of COVID-19. Since the amount of testing kits for the diagnosis is insufficient and the conventional diagnosis methods are slow, developing AI-based fast diagnosis tools is not only an alternative way but also an urgent requirement for such alarming situations as those people faced with today. In this study, we employed three popular CNN models, VGG16, VGG19, and Xception, to classify CT scans of suspected patient cases as COVID-19 infected and non-COVID-19. VGG16 achieved 93% accuracy with the best parameters on the test set.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127741321","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: 6
A Machine Learning-Based Approach to Detect Survival of Heart Failure Patients 一种基于机器学习的心力衰竭患者生存率检测方法
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299320
Ç. Erdaş, Didem Ölçer
{"title":"A Machine Learning-Based Approach to Detect Survival of Heart Failure Patients","authors":"Ç. Erdaş, Didem Ölçer","doi":"10.1109/TIPTEKNO50054.2020.9299320","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299320","url":null,"abstract":"One of the diseases with high prevalence among the consequences of cardiovascular diseases is heart failure. Heart failure is a condition in which the muscles in the heart wall become faded and dilated, limiting the heart’s ability to pump blood. As time passes, the heart cannot meet the proper blood requirement in the body, and as a result, the person has difficulty breathing. As the human age increases, the incidence of heart failure gradually increases, and the rate of mortality due to heart failure also increases. In this context, close monitoring of people suffering from this disease will significantly increase the survival rate. In this study, a machine learning-based system is proposed to predict the mortality-survival status of patients with heart failure. Thus, by identifying people with mortality risk, the survival probability of the patients may increase with more effective and close follow-up.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927308","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: 4
Magnetic levitation-based adipose tissue engineering using horizontal magnet deployment 基于磁悬浮的脂肪组织工程水平磁铁部署
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299312
Oyku Sarigil, Muge Anil-Inevi, Esra Yılmaz, Ozge S Ozcelik, Gulistan Mese, H. Tekin, E. Ozcivici
{"title":"Magnetic levitation-based adipose tissue engineering using horizontal magnet deployment","authors":"Oyku Sarigil, Muge Anil-Inevi, Esra Yılmaz, Ozge S Ozcelik, Gulistan Mese, H. Tekin, E. Ozcivici","doi":"10.1109/TIPTEKNO50054.2020.9299312","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299312","url":null,"abstract":"Magnetic levitation is a promising technique for tissue engineering with contact- and label-free approach. Levitation-based biofabrication systems emerge as a simple, rapid and versatile alternative to traditional tissue culture systems, since biofabrication specs can easily be tailored via magnet shape and configuration. This study aims at possible magnetic levitation systems for culture of adipose tissue cells. In this study, we performed two different magnet configurations, vertical and horizontal deployment, in an effort to be utilized in adipose tissue engineering.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124895882","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 Cardiac Arrhythmia using Autonomic Nervous System, Gaussian Mixture Model and Artificial Neural Network 应用自主神经系统、高斯混合模型和人工神经网络检测心律失常
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299274
M. B. Terzi, V. Arikan
{"title":"Detection of Cardiac Arrhythmia using Autonomic Nervous System, Gaussian Mixture Model and Artificial Neural Network","authors":"M. B. Terzi, V. Arikan","doi":"10.1109/TIPTEKNO50054.2020.9299274","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299274","url":null,"abstract":"In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) by using state-of-the-art signal processing and machine learning methods is developed to perform the robust detection of cardiac arrhythmia (CA). For this purpose, a signal processing technique that simultaneously obtains SKNA and ECG from wideband recordings on MIT-BIH database is developed. By using preprocessed data, a novel feature extraction technique which obtains SKNA features that are critical for the reliable detection of CA is developed. By using extracted features, a supervised learning technique based on artificial neural network (ANN) and an unsupervised learning technique based on Gaussian mixture model (GMM) are developed to perform the robust detection of SKNA anomalies. A Neyman-Pearson type of approach is developed to perform the robust detection of outliers that correspond to CA. The performance results of the proposed technique over MIT-BIH database showed that the technique provides highly reliable detection of CA by performing the robust detection of SKNA anomalies. Therefore, in cases where the diagnostic information of ECG is not sufficient for the reliable diagnosis of CA, the proposed technique can provide early diagnosis of the disease, which can lead to a significant reduction in the mortality rates of cardiovascular diseases.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122553188","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 Preliminary Study on Cell Motility Analysis from Phase-Contrast Microscopy Image Series 从相衬显微镜图像序列分析细胞运动的初步研究
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299319
Emre Kayan, ve Tarık Kavuşan, Sevgi Önal, D. P. Okvur, ve Özden Y. Özuysal, B. U. Töreyin, D. Ünay
{"title":"A Preliminary Study on Cell Motility Analysis from Phase-Contrast Microscopy Image Series","authors":"Emre Kayan, ve Tarık Kavuşan, Sevgi Önal, D. P. Okvur, ve Özden Y. Özuysal, B. U. Töreyin, D. Ünay","doi":"10.1109/TIPTEKNO50054.2020.9299319","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299319","url":null,"abstract":"Analyses of morphology, polarity, and motility of cells is important for cell biology research such as metastatic and invasive capacity of cells, wound healing, and embryonic development. Automation of such analyses using image series of phase-contrast optical microscopy, which allows label-free imaging of live cells in their living environment, is a need. With this purpose, in this study image series of a cell motility experiment is manually annotated, and an automation algorithm realizing motion and shape analyses of cells using the annotated data is developed. In addition, due to the low number of annotated data at hand, a U-Net based solution is devised for automated segmentation of the cells and its performance is evaluated.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117208591","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 Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images 基于卷积神经网络特征的多类胸部x线图像新冠肺炎检测
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299289
A. Narin
{"title":"Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images","authors":"A. Narin","doi":"10.1109/TIPTEKNO50054.2020.9299289","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299289","url":null,"abstract":"Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the covid19 attacked the respiratory systems. Classification performances with support vector machines have been obtained by using the features extracted with residual networks (ResNet-50), one of the convolutional neural network models, from these images. While Covid-19 detection is obtained with support vector machines (SVM)-quadratic with the highest sensitivity value of 96.35% with the 5-fold cross-validation method, the highest overall performance value has been detected with both SVM-quadratic and SVM-cubic above 99%. According to these high results, it is thought that this method, which has been studied, will help radiology specialists and reduce the rate of false detection.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123684102","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: 17
Covid-19 Classification Using Deep Learning in Chest X-Ray Images 在胸部x射线图像中使用深度学习进行Covid-19分类
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299315
Z. Karhan, F. Akal
{"title":"Covid-19 Classification Using Deep Learning in Chest X-Ray Images","authors":"Z. Karhan, F. Akal","doi":"10.1109/TIPTEKNO50054.2020.9299315","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299315","url":null,"abstract":"Covid-19 virus, which has emerged in the Republic of China in an undetermined cause, has affected the whole world quickly. It is important to detect positive cases early to prevent further spread of the outbreak. In the diagnostic phase, radiological images of the chest are determinative as well as the RT-PCR (Reverse Transcription-Polymerase Chain Reaction) test. It was classified with the ResNet50 model, which is a convolutional neural network architecture in Covid-19 detection using chest x-ray images. Chest X-Ray image analysis can be done and infected individuals can be identified thanks to artificial intelligence quickly. The experimental results are encouraging in terms of the use of computer-aided in the field of pathology. It can also be used in situations where the possibilities and RT-PCR tests are insufficient.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127798034","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: 29
EEG based Epileptic Seizures Detection using Intrinsic Time-Scale Decomposition 基于内禀时间尺度分解的脑电图癫痫发作检测
2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299262
Murside Degirmenci, A. Akan
{"title":"EEG based Epileptic Seizures Detection using Intrinsic Time-Scale Decomposition","authors":"Murside Degirmenci, A. Akan","doi":"10.1109/TIPTEKNO50054.2020.9299262","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299262","url":null,"abstract":"Epilepsy is a type of neurological disorder that causes abnormal brain activities and creates epileptic seizures. Traditionally epileptic seizure prediction is realized with a visual examination of Electroencephalogram (EEG) signals. But this technique needs a long time EEG monitoring. So, the automatic epileptic seizures prediction schemes become a requirement at this point. This study proposes a method to classify epileptic seizures and normal EEG data by utilizing the Intrinsic Time-scale Decomposition (ITD)-based features. The dataset has been supplied from the database of the Epileptology Department of Bonn University. It contains 5 data groups A, B, C, D, E. The study aims to classify healthy and epileptic data, so data of groups A and E are used to perform evaluations of proposed methods. The EEG data are decomposed into Proper Rotation Components (PRCs) by ITD. The feature extraction methods are applied to the first five PRCs of each EEG data from healthy and epileptic individuals. These features are classified using K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes, Support Vector Machine (SVM) and Logistic Regression classifiers. The results demonstrated that the epileptic data is differentiated from normal data by applying the nonlinear ITD with outstanding classification performance.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116178382","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: 2
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