Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299262
Murside Degirmenci, A. Akan
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.
癫痫是一种神经系统疾病，会导致大脑活动异常，并导致癫痫发作。传统的癫痫发作预测是通过脑电图(EEG)信号的视觉检查来实现的。但该技术需要长时间的脑电图监测。因此，癫痫发作的自动预测方案在这一点上成为一种需求。本研究提出了一种利用固有时间尺度分解(ITD)特征对癫痫发作和正常脑电图数据进行分类的方法。数据集来自波恩大学癫痫学系的数据库。它包含A, B, C, D, E 5组数据。本研究的目的是对健康数据和癫痫数据进行分类，因此使用A组和E组的数据对所提出的方法进行评估。利用ITD将脑电数据分解为适当旋转分量(PRCs)。将特征提取方法应用于健康和癫痫个体的每个EEG数据的前五个prc。这些特征使用k近邻(KNN)、线性判别分析(LDA)、朴素贝叶斯、支持向量机(SVM)和逻辑回归分类器进行分类。结果表明，应用非线性过渡段可将癫痫数据与正常数据区分开来，分类效果较好。
{"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}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299220
Sabri Can Cetindag, Kubilay Guran, G. Bilgin
As the technological advances in computer hardware and machine learning have increased significantly, deep learning models have also been used in many different areas. Examples of these areas are image recognition, face detection, natural language processing, toxicology, suggestion systems, anomaly detection and disease diagnosis in the health sector. This study focuses on studies on disease prediction and diagnosis through histopathological images. The main purpose of the study is to apply deep learning models that can classify cancerous tissues with high accuracy. Besides that, implementation of deep models are done with a low computational cost so that models can be trained in a fast manner. Within the scope of this subject, the convolutional neural network models, which are very popular in image classification in the deep learning world, have been realized by applying transfer learning technique. In addition to these models, a deep learning model called CAT-Net is used to compare and evaluate the success of the transfer learning method. The results of the study are compared with overall accuracy, precision, recall, and F1 score metrics for each model.
{"title":"Transfer Learning Methods for Using Textural Features in Histopathological Image Classification","authors":"Sabri Can Cetindag, Kubilay Guran, G. Bilgin","doi":"10.1109/TIPTEKNO50054.2020.9299220","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299220","url":null,"abstract":"As the technological advances in computer hardware and machine learning have increased significantly, deep learning models have also been used in many different areas. Examples of these areas are image recognition, face detection, natural language processing, toxicology, suggestion systems, anomaly detection and disease diagnosis in the health sector. This study focuses on studies on disease prediction and diagnosis through histopathological images. The main purpose of the study is to apply deep learning models that can classify cancerous tissues with high accuracy. Besides that, implementation of deep models are done with a low computational cost so that models can be trained in a fast manner. Within the scope of this subject, the convolutional neural network models, which are very popular in image classification in the deep learning world, have been realized by applying transfer learning technique. In addition to these models, a deep learning model called CAT-Net is used to compare and evaluate the success of the transfer learning method. The results of the study are compared with overall accuracy, precision, recall, and F1 score metrics for each model.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"49 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":"131188849","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}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299315
Z. Karhan, F. Akal
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.
{"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}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299298
Ugur Sahin, E. Budak, O. Eroğul
PSG (polysomnography) is a multi-parameter test used in sleep studies and sleep medicine. It is mostly used in the diagnosis of sleep respiratory disorders also PSG test can be used in the diagnosis of diseases such as sleep terror, restless leg syndrome, sleep paralysis and narcolepsy. The patient, who is subjected to the PSG test, spends the night in a sleep laboratory under the supervision of a specialist physician and technician. The PSG test is very expensive and there are a limited number of PSG devices and a limited number of sleep technicians for this test in sleep centers. Even if an attempt is made to prepare an environment close to the home, the sleep test performed in a laboratory environment cast out the patient from the natural sleep environment. For these reasons and more, devices have been manufactured to reduce the cost of tests, perform the test in the sleep environment that the patient is accustomed to, and serve more patients at home. In this study, an Iot based, wearable test device was developed that allows patients with sleep disorders to perform sleep tests at home, at lower costs, without the need for a sleep technician.
{"title":"Design of a Wireless Polysomnography System","authors":"Ugur Sahin, E. Budak, O. Eroğul","doi":"10.1109/TIPTEKNO50054.2020.9299298","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299298","url":null,"abstract":"PSG (polysomnography) is a multi-parameter test used in sleep studies and sleep medicine. It is mostly used in the diagnosis of sleep respiratory disorders also PSG test can be used in the diagnosis of diseases such as sleep terror, restless leg syndrome, sleep paralysis and narcolepsy. The patient, who is subjected to the PSG test, spends the night in a sleep laboratory under the supervision of a specialist physician and technician. The PSG test is very expensive and there are a limited number of PSG devices and a limited number of sleep technicians for this test in sleep centers. Even if an attempt is made to prepare an environment close to the home, the sleep test performed in a laboratory environment cast out the patient from the natural sleep environment. For these reasons and more, devices have been manufactured to reduce the cost of tests, perform the test in the sleep environment that the patient is accustomed to, and serve more patients at home. In this study, an Iot based, wearable test device was developed that allows patients with sleep disorders to perform sleep tests at home, at lower costs, without the need for a sleep technician.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"90 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":"133458723","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}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299317
Ozlem Karabiber Cura, A. Akan
Epilepsy is one of the neurological diseases that occur incidences worldwide. The electroencephalography (EEG) recording method is the most frequently used clinical practice in the diagnosis and monitoring of epilepsy. Many computer-aided analysis methods have been developed in the literature to facilitate the analysis of long-term EEG signals. In the proposed study, the patient-based seizure detection approach is proposed using a high-resolution time-frequency (TF) representation named Synchrosqueezed Transform (SST) method. The SST of two different data sets called the IKCU data set and CHB-MIT data set are obtained, and Higher-order joint TF(HOJ-TF) based and Gray-level co-occurrence matrix (GLCM) based features are calculated using these SSTs. Using some machine learning methods such as Decision Tree (DT), k-Nearest Neighbor (kNN), and Logistic Regression (LR), classification processes are conducted. High patient-based seizure detection success is achieved for both the IKCU data set (94.25%) and the CHB-MIT data set (95.15%).
{"title":"Epileptic EEG Classification Using Synchrosqueezing Transform and Machine Learning","authors":"Ozlem Karabiber Cura, A. Akan","doi":"10.1109/TIPTEKNO50054.2020.9299317","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299317","url":null,"abstract":"Epilepsy is one of the neurological diseases that occur incidences worldwide. The electroencephalography (EEG) recording method is the most frequently used clinical practice in the diagnosis and monitoring of epilepsy. Many computer-aided analysis methods have been developed in the literature to facilitate the analysis of long-term EEG signals. In the proposed study, the patient-based seizure detection approach is proposed using a high-resolution time-frequency (TF) representation named Synchrosqueezed Transform (SST) method. The SST of two different data sets called the IKCU data set and CHB-MIT data set are obtained, and Higher-order joint TF(HOJ-TF) based and Gray-level co-occurrence matrix (GLCM) based features are calculated using these SSTs. Using some machine learning methods such as Decision Tree (DT), k-Nearest Neighbor (kNN), and Logistic Regression (LR), classification processes are conducted. High patient-based seizure detection success is achieved for both the IKCU data set (94.25%) and the CHB-MIT data set (95.15%).","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":"122429040","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}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299217
G. Uysal, M. Ozturk
Early detection of the stage of mild cognitive impairment (MCI) is very important for early diagnosis of dementia and slowing down the progression of Alzheimer’s disease. Atrophy values obtained by magnetic resonance imaging (MRI), one of the neuroimaging techniques, are considered to be a fairly powerful diagnostic biomarker used in the detection of Alzheimer. Since the transition from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI) is irreversible and implies a significant change in a patient’s condition, we focus on to the classification of these two stages in this work. In this study, atrophy values of 13 brain areas of 90 early mild cognitive impairment, 38 late mild cognitive impairment, 14 mild cognitive impairment participants were used in the diagnosis of the disease. Diagnosis groups have been classified with an accuracy of 68.8% as a result of data estimations obtained using classification algorithms. When the classification has been made only by taking effective values, an accuracy rate of 75% has been achieved and this means a significative improvement. The deep analysis of the disease and the focusing on the brain regions where it has more impact in order to distinguish the stages early, show the potential of utilizing MRI features to improve cognitive assessment.
{"title":"Classifying Early and Late Mild Cognitive Impairment Stages of Alzheimer’s Disease by Analyzing Different Brain Areas","authors":"G. Uysal, M. Ozturk","doi":"10.1109/TIPTEKNO50054.2020.9299217","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299217","url":null,"abstract":"Early detection of the stage of mild cognitive impairment (MCI) is very important for early diagnosis of dementia and slowing down the progression of Alzheimer’s disease. Atrophy values obtained by magnetic resonance imaging (MRI), one of the neuroimaging techniques, are considered to be a fairly powerful diagnostic biomarker used in the detection of Alzheimer. Since the transition from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI) is irreversible and implies a significant change in a patient’s condition, we focus on to the classification of these two stages in this work. In this study, atrophy values of 13 brain areas of 90 early mild cognitive impairment, 38 late mild cognitive impairment, 14 mild cognitive impairment participants were used in the diagnosis of the disease. Diagnosis groups have been classified with an accuracy of 68.8% as a result of data estimations obtained using classification algorithms. When the classification has been made only by taking effective values, an accuracy rate of 75% has been achieved and this means a significative improvement. The deep analysis of the disease and the focusing on the brain regions where it has more impact in order to distinguish the stages early, show the potential of utilizing MRI features to improve cognitive assessment.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"168 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":"122029923","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}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299213
Berkay Mayalıve, Orkun Şaylığ, Ö. Y. Özuysal, D. P. Okvur, B. U. Töreyin, D. Ünay
Collective cell analysis from microscopy image series is important for wound healing research. Computer-based automation of such analyses may help in rapid acquisition of reliable and reproducible results. In this study phase-contrast optical microscopy image series of an in-vitro wound healing essay is manually delineated by two experts and its analysis is realized, traditional image processing and deep learning based approaches for automated segmentation of wound area are developed and their performance comparisons are carried out.
{"title":"Automated Analysis of Wound Healing Microscopy Image Series - A Preliminary Study","authors":"Berkay Mayalıve, Orkun Şaylığ, Ö. Y. Özuysal, D. P. Okvur, B. U. Töreyin, D. Ünay","doi":"10.1109/TIPTEKNO50054.2020.9299213","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299213","url":null,"abstract":"Collective cell analysis from microscopy image series is important for wound healing research. Computer-based automation of such analyses may help in rapid acquisition of reliable and reproducible results. In this study phase-contrast optical microscopy image series of an in-vitro wound healing essay is manually delineated by two experts and its analysis is realized, traditional image processing and deep learning based approaches for automated segmentation of wound area are developed and their performance comparisons are carried out.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"6 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":"122091687","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}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299276
Rabia Gul, Saika Sener, E. Hocaoğlu
This study presents a two degrees of freedom (DoF) parallel manipulator that enables Parkinsonian Patients to regularly do the assigned rhythmic tasks in order to reduce the symptoms of motor disorders. Considering the elderly patients who constitute the majority of the Parkinsonians, the robot is designed to be portable to serve people to consistently take therapy at home. Moreover, the robotic platform is designed to be adjustable for any anthropometric size of a human arm in order to allow people to ergonomically perform tasks. The kinematic analysis and control of the five-bar parallel robot are carried out to ensure that users can do upper extremity coordination on the anthropometrically compatible workspace.
{"title":"A Mobile Parallel Manipulator for the Elbow Rehabilitation of Parkinsonian Patients","authors":"Rabia Gul, Saika Sener, E. Hocaoğlu","doi":"10.1109/TIPTEKNO50054.2020.9299276","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299276","url":null,"abstract":"This study presents a two degrees of freedom (DoF) parallel manipulator that enables Parkinsonian Patients to regularly do the assigned rhythmic tasks in order to reduce the symptoms of motor disorders. Considering the elderly patients who constitute the majority of the Parkinsonians, the robot is designed to be portable to serve people to consistently take therapy at home. Moreover, the robotic platform is designed to be adjustable for any anthropometric size of a human arm in order to allow people to ergonomically perform tasks. The kinematic analysis and control of the five-bar parallel robot are carried out to ensure that users can do upper extremity coordination on the anthropometrically compatible workspace.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"50 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":"127692816","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}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299301
Mertcan Özdemir, E. Budak, O. Eroğul
Patient monitor modules have various inputs for many vital function measurements. Blood pressure measurement, one of the most important of these measurements, is included in the biomedical engineering field. This study is about the design and implementation of a programmable invasive blood pressure simulator. This device can generate the programmable behavior of the voltage signal corresponding to the blood pressure curve. The user communication interface of the device allows to select the type of signal produced with the LCD and 3 buttons. Broad spectrum of the generated signals corresponding to physiological or pathological blood pressure curves are stored in a programmable memory. The input and output connectors of the device can be directly connected to a patient monitor or IBP Kit to IBP module input. Invasive blood pressure measurement simulation can be used in IBP Kits and monitors developed for training and calibration purposes.
{"title":"Development of Invasive Blood Pressure Simulator Design for Testing and Calibrating","authors":"Mertcan Özdemir, E. Budak, O. Eroğul","doi":"10.1109/TIPTEKNO50054.2020.9299301","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299301","url":null,"abstract":"Patient monitor modules have various inputs for many vital function measurements. Blood pressure measurement, one of the most important of these measurements, is included in the biomedical engineering field. This study is about the design and implementation of a programmable invasive blood pressure simulator. This device can generate the programmable behavior of the voltage signal corresponding to the blood pressure curve. The user communication interface of the device allows to select the type of signal produced with the LCD and 3 buttons. Broad spectrum of the generated signals corresponding to physiological or pathological blood pressure curves are stored in a programmable memory. The input and output connectors of the device can be directly connected to a patient monitor or IBP Kit to IBP module input. Invasive blood pressure measurement simulation can be used in IBP Kits and monitors developed for training and calibration purposes.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"33 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":"129112771","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}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299295
Muharrem Çelebi, Kemal Güllü
The aim of this study is to determine the appropriate window size and windowing function for studies related to epileptic seizure forecasting. Firstly, in order to accomplish this aim, a suitable data set is obtained. Afterwards, tests are performed for 12 different window durations and the most suitable windowing time is determined. Determined window duration, windowing functions of 5 different properties are applied and performance rates are examined. As a result of the findings obtained in future studies, it is aimed to increase the success rate by conducting test operations with different features and classifiers.
{"title":"Determining Appropriate Window Size and Window Function for Epileptic Seizure Forecasting","authors":"Muharrem Çelebi, Kemal Güllü","doi":"10.1109/TIPTEKNO50054.2020.9299295","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299295","url":null,"abstract":"The aim of this study is to determine the appropriate window size and windowing function for studies related to epileptic seizure forecasting. Firstly, in order to accomplish this aim, a suitable data set is obtained. Afterwards, tests are performed for 12 different window durations and the most suitable windowing time is determined. Determined window duration, windowing functions of 5 different properties are applied and performance rates are examined. As a result of the findings obtained in future studies, it is aimed to increase the success rate by conducting test operations with different features and classifiers.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"1 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":"129138710","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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