2020 Medical Technologies Congress (TIPTEKNO)最新文献_第9页

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

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

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

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

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

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

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

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