Pub Date : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479764
Zeynep Colak, S. Helhel, I. Basyigit, Ş. Özen
In this study, two different mobile communication operators provide services in Turkey have been chosen that each operator has both 2G and 3G services. In this study, electromagnetic interference distance to medical equipments located in The Medical School Hospital of Akdeniz University sourced from mobiled phones have been examined. Through out different units in the hospital environment, 30 different measurements carried out, and deterioration in audio and visual signal reaction of devices was found to be associated with the distance to mobile phones. Electromagnetic interference, particularly of the ECG and ted EEG device was observed, and exposure begins with range 1.25m distance.
{"title":"Safety distance for medical equipments based on 2G and 3G mobile systems","authors":"Zeynep Colak, S. Helhel, I. Basyigit, Ş. Özen","doi":"10.1109/BIYOMUT.2010.5479764","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479764","url":null,"abstract":"In this study, two different mobile communication operators provide services in Turkey have been chosen that each operator has both 2G and 3G services. In this study, electromagnetic interference distance to medical equipments located in The Medical School Hospital of Akdeniz University sourced from mobiled phones have been examined. Through out different units in the hospital environment, 30 different measurements carried out, and deterioration in audio and visual signal reaction of devices was found to be associated with the distance to mobile phones. Electromagnetic interference, particularly of the ECG and ted EEG device was observed, and exposure begins with range 1.25m distance.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120959948","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 : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479863
Devrim Önder, S. Sarıoğlu, Bilge Karaçali
The aim of this work is to perform automated texture classification of histology slide images in health and cancerous conditions using quasi-supervised statistical learning method. Tissue images were acquired from histological slides of human colon and were separated into two groups in terms of normal and disease conditions. Texture feature vectors corresponding to tissue segments of each image were calculated using co-occurrence matrices. Different texture regions were determined by the quasi-supervised statistical learning method using texture features of normal and cancerous groups.
{"title":"Automated classification of cancerous textures in histology images using quasi-supervised learning algorithm","authors":"Devrim Önder, S. Sarıoğlu, Bilge Karaçali","doi":"10.1109/BIYOMUT.2010.5479863","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479863","url":null,"abstract":"The aim of this work is to perform automated texture classification of histology slide images in health and cancerous conditions using quasi-supervised statistical learning method. Tissue images were acquired from histological slides of human colon and were separated into two groups in terms of normal and disease conditions. Texture feature vectors corresponding to tissue segments of each image were calculated using co-occurrence matrices. Different texture regions were determined by the quasi-supervised statistical learning method using texture features of normal and cancerous groups.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121846554","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 : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479850
T. Uçar, D. Karahoca, A. Karahoca
A correct diagnosis of tuberculosis can be only stated by applying a medical test to patient's phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining solution which makes diagnosis of tuberculosis as accurate as possible and helps deciding if it is reasonable to start tuberculosis treatment on suspected patients without waiting the exact medical test results. In this research, we compared the use of Bayesian Networks and Rough Sets to predict the existence of mycobacterium tuberculosis. 503 different patient records having 30 separate input parameters are obtained from a private clinic and used in the entire process of this research. The Bayesian Network model classifies the instances with RMSE of 22% whereas Rough Set algorithm does the same classification with RMSE of 37%. As a result, Bayesian Network is an accurate and reliable method when compared with Rough Set method for classification of tuberculosis patients.
{"title":"Predicting the existence of mycobacterium tuberculosis infection by Bayesian Networks and Rough Sets","authors":"T. Uçar, D. Karahoca, A. Karahoca","doi":"10.1109/BIYOMUT.2010.5479850","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479850","url":null,"abstract":"A correct diagnosis of tuberculosis can be only stated by applying a medical test to patient's phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining solution which makes diagnosis of tuberculosis as accurate as possible and helps deciding if it is reasonable to start tuberculosis treatment on suspected patients without waiting the exact medical test results. In this research, we compared the use of Bayesian Networks and Rough Sets to predict the existence of mycobacterium tuberculosis. 503 different patient records having 30 separate input parameters are obtained from a private clinic and used in the entire process of this research. The Bayesian Network model classifies the instances with RMSE of 22% whereas Rough Set algorithm does the same classification with RMSE of 37%. As a result, Bayesian Network is an accurate and reliable method when compared with Rough Set method for classification of tuberculosis patients.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123746522","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 : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479741
Pinar Ozel, Ferda Ilgen Uslu, A. Deniz Duru, S. Burcu Erdogan, A. Gokyigit, A. Ademoglu
Stroke is one of the most significant public health problems. It is the third cause of mortality cause and first cause of paralysis in developed societies. Diagnosis of stroke is determined using clinical symptoms and several imaging modalities. Magnetic Resonance Imaging is one of these methods. Images obtained with these modalities are used by the physician on a routine clinical investigation. However, in some cases, the boundaries of the stroke tissue should be selected and mapped to the anatomical regions in the atlas. In this study, a graphical user interface is developed in order to identify and map the stroke regions to the digital anatomical atlas on registered and normalized MR images by SPM5. By using this interface, it is aimed to investigate the MR images of the stroke patients and perform specific therapy planning for different groups.
{"title":"Determination and anatomical mapping of thalamic stroke regions to anatomical atlas","authors":"Pinar Ozel, Ferda Ilgen Uslu, A. Deniz Duru, S. Burcu Erdogan, A. Gokyigit, A. Ademoglu","doi":"10.1109/BIYOMUT.2010.5479741","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479741","url":null,"abstract":"Stroke is one of the most significant public health problems. It is the third cause of mortality cause and first cause of paralysis in developed societies. Diagnosis of stroke is determined using clinical symptoms and several imaging modalities. Magnetic Resonance Imaging is one of these methods. Images obtained with these modalities are used by the physician on a routine clinical investigation. However, in some cases, the boundaries of the stroke tissue should be selected and mapped to the anatomical regions in the atlas. In this study, a graphical user interface is developed in order to identify and map the stroke regions to the digital anatomical atlas on registered and normalized MR images by SPM5. By using this interface, it is aimed to investigate the MR images of the stroke patients and perform specific therapy planning for different groups.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121495701","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 : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479848
Baycan Akcay, M. Engin, E. Z. Engin, Seyhan Coskun, Gungor Polat
In this study, the time-scale based analysis of EEG signals is shown for recognition of sleep stages. The EEG signals from healthy subjects are analyzed by Scalogram method in the time-scale domain. We observed that statistical parameters, the average gray level and measure of uniformity extracted from the energy distribution images, are found to be effective on the recognition of sleep stages.
{"title":"Investigation of sleep stages identification with time-scale based parameters","authors":"Baycan Akcay, M. Engin, E. Z. Engin, Seyhan Coskun, Gungor Polat","doi":"10.1109/BIYOMUT.2010.5479848","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479848","url":null,"abstract":"In this study, the time-scale based analysis of EEG signals is shown for recognition of sleep stages. The EEG signals from healthy subjects are analyzed by Scalogram method in the time-scale domain. We observed that statistical parameters, the average gray level and measure of uniformity extracted from the energy distribution images, are found to be effective on the recognition of sleep stages.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131136561","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 : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479847
F. Ates, U. Akgun, M. Karahan, C. Yucesoy
In this study, it is aimed at measuring for the first time the isometric force of human gracilis (G) muscle as a function of joint angle, intraoperatively. Experiments were conducted during anterior cruciate ligament reconstruction surgery. The knee angle was fixed at 120°, 90°, 60°, 30° and 0° respectively and active isometric forces of this muscle were measured using a buckle force transducer. Limited correlation was found between the anthropometric data of the subjects and the maximal G muscle force. Accordingly, we suggest that in interventions targeting G muscle, a patient specific approach needs to be planned for achieving optimal results. G muscle was shown to be functional for almost all of the knee angle range studied. This result indicates that G muscle contributes to the knee moment for even very low muscle lengths during major daily activities including walking and sit-to-stand motion.
{"title":"Intraoperative measurement of human gracilis muscle isometric forces as a function of knee angle","authors":"F. Ates, U. Akgun, M. Karahan, C. Yucesoy","doi":"10.1109/BIYOMUT.2010.5479847","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479847","url":null,"abstract":"In this study, it is aimed at measuring for the first time the isometric force of human gracilis (G) muscle as a function of joint angle, intraoperatively. Experiments were conducted during anterior cruciate ligament reconstruction surgery. The knee angle was fixed at 120°, 90°, 60°, 30° and 0° respectively and active isometric forces of this muscle were measured using a buckle force transducer. Limited correlation was found between the anthropometric data of the subjects and the maximal G muscle force. Accordingly, we suggest that in interventions targeting G muscle, a patient specific approach needs to be planned for achieving optimal results. G muscle was shown to be functional for almost all of the knee angle range studied. This result indicates that G muscle contributes to the knee moment for even very low muscle lengths during major daily activities including walking and sit-to-stand motion.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134366553","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 : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479800
Güray Gürkan, A. Uslu, Bora Cebeci, E. Erdogan, Itir Kasikci, T. O. Seyhan, A. Akan, T. Demiralp
In this study, we present the spatial and temporal evolution of EEG signal spectrum under anaesthesia. Studied features include SEF-90, α-β power ratios, spectral entropy that are known to be used in commercially available depth of anaesthesia monitors. As an additional and comparing feature, we also present Higuchi fractal dimension that is used for analysis of non-linear systems. By means of spatial analysis, we verified the shift of occipitally dominant alpha activity to frontal regions and demonstrated corresponding topographic plots.
{"title":"Topographic and temporal spectral analysis of EEG signals during anaesthesia","authors":"Güray Gürkan, A. Uslu, Bora Cebeci, E. Erdogan, Itir Kasikci, T. O. Seyhan, A. Akan, T. Demiralp","doi":"10.1109/BIYOMUT.2010.5479800","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479800","url":null,"abstract":"In this study, we present the spatial and temporal evolution of EEG signal spectrum under anaesthesia. Studied features include SEF-90, α-β power ratios, spectral entropy that are known to be used in commercially available depth of anaesthesia monitors. As an additional and comparing feature, we also present Higuchi fractal dimension that is used for analysis of non-linear systems. By means of spatial analysis, we verified the shift of occipitally dominant alpha activity to frontal regions and demonstrated corresponding topographic plots.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116166260","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 : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479777
G. Şengül, U. Baysal
In this study a fully automatic fotogrammetric system is designed to determine the EEG electrode positions in 3D. The proposed system uses a 1.3 MP web camera rotating over the subject's head. The camera is driven by a step motor. The camera takes photos in every 7.20 angles during the rotation. In order to realize full automation, electrodes are labeled by colored circular markers and an electrode identification algorithm is develeoped for full automation. The proposed method is tested by using a realistic head phantom carrying 25 electrodes. The positions of the test electrodes are also measured by a conventional 3-D digitizer. The measurements are repeated 3 times for repeatibility purposes. It is found that 3-d digitizer localizes the electrodes with an average error of 8.46 mm, 7.63 mm and 8.32 mm, while the proposed system localizes the electrodes with an average error of 1.76 mm, 1.42 mm and 1.53 mm.
{"title":"A fully automatic photogrammetric system design using a 1.3 MP web camera to determine EEG electrode positions","authors":"G. Şengül, U. Baysal","doi":"10.1109/BIYOMUT.2010.5479777","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479777","url":null,"abstract":"In this study a fully automatic fotogrammetric system is designed to determine the EEG electrode positions in 3D. The proposed system uses a 1.3 MP web camera rotating over the subject's head. The camera is driven by a step motor. The camera takes photos in every 7.20 angles during the rotation. In order to realize full automation, electrodes are labeled by colored circular markers and an electrode identification algorithm is develeoped for full automation. The proposed method is tested by using a realistic head phantom carrying 25 electrodes. The positions of the test electrodes are also measured by a conventional 3-D digitizer. The measurements are repeated 3 times for repeatibility purposes. It is found that 3-d digitizer localizes the electrodes with an average error of 8.46 mm, 7.63 mm and 8.32 mm, while the proposed system localizes the electrodes with an average error of 1.76 mm, 1.42 mm and 1.53 mm.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125770270","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 : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479860
Yasin Güven, D. Barkana
Recent research in orthopedic surgeries indicates that computerassisted robotic systems have shown that robots may improve the precision and accuracy of the surgery which in turn leads to better long-term outcomes. An orthopedic robotic system called OrthoRoby and an intelligent control architecture that will be used in bone cutting operations were developed. In this paper, a medical user interface was developed and integrated into the OrthoRoby system. Medical user interface used Computed Tomography (CT) images of the patients' bone.
{"title":"Medical user interface for orthopedical surgical robotic system","authors":"Yasin Güven, D. Barkana","doi":"10.1109/BIYOMUT.2010.5479860","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479860","url":null,"abstract":"Recent research in orthopedic surgeries indicates that computerassisted robotic systems have shown that robots may improve the precision and accuracy of the surgery which in turn leads to better long-term outcomes. An orthopedic robotic system called OrthoRoby and an intelligent control architecture that will be used in bone cutting operations were developed. In this paper, a medical user interface was developed and integrated into the OrthoRoby system. Medical user interface used Computed Tomography (CT) images of the patients' bone.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130276453","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 : 2010-04-21DOI: 10.1109/BIYOMUT.2010.5479842
Umut Orhan, M. Hekim, M. Özer
Electroencephalogram (EEG) recording systems have been frequently used as the sources of information in diagnosis of epilepsy by several researchers. In this study, rearranged EEG signals were classified by Multilayer Perceptron Neural Network (MLPNN) model. Used data consists of five groups (A, B, C, D, and E) each containing 100 EEG segments. In this study, center points with equal interval were selected on amplitude axis of each EEG segment. EEG signals were rearranged by way of that each amplitude value was shifted to the center point closest to itself. Equal width discretization (EWD) method was used for rearrangement process. Wavelet coefficients of each segment of EEG signals were computed by using discrete wavelet transform (DWT). The mean, the standard deviation and the entropy of these coefficients was used as the inputs of MLPNN model. The model was protected from the overfitting by cross validation. Two different classification experiments were implemented by the same MLPNN model: 1) the classification of healthy volunteers, epilepsy patients during seizure and epilepsy patients during a seizure-free interval, 2) the classification of epilepsy patients during seizure and seizure-free interval. MLPNN model classified EEG signals with the accuracy of 99.60% in first experiment and 100% in second experiment. It is observed that MLPNN classification of EEG signals after rearrangement in amplitude axis provides better results.
{"title":"Discretization approach to EEG signal classification using Multilayer Perceptron Neural Network model","authors":"Umut Orhan, M. Hekim, M. Özer","doi":"10.1109/BIYOMUT.2010.5479842","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2010.5479842","url":null,"abstract":"Electroencephalogram (EEG) recording systems have been frequently used as the sources of information in diagnosis of epilepsy by several researchers. In this study, rearranged EEG signals were classified by Multilayer Perceptron Neural Network (MLPNN) model. Used data consists of five groups (A, B, C, D, and E) each containing 100 EEG segments. In this study, center points with equal interval were selected on amplitude axis of each EEG segment. EEG signals were rearranged by way of that each amplitude value was shifted to the center point closest to itself. Equal width discretization (EWD) method was used for rearrangement process. Wavelet coefficients of each segment of EEG signals were computed by using discrete wavelet transform (DWT). The mean, the standard deviation and the entropy of these coefficients was used as the inputs of MLPNN model. The model was protected from the overfitting by cross validation. Two different classification experiments were implemented by the same MLPNN model: 1) the classification of healthy volunteers, epilepsy patients during seizure and epilepsy patients during a seizure-free interval, 2) the classification of epilepsy patients during seizure and seizure-free interval. MLPNN model classified EEG signals with the accuracy of 99.60% in first experiment and 100% in second experiment. It is observed that MLPNN classification of EEG signals after rearrangement in amplitude axis provides better results.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129440467","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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