Yazar "Özşen S." seçeneğine göre listele

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  • Küçük Resim Yok
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    Detection of the electrode disconnection in sleep signals [Uyku Sinyallerindeki Elektrod Kopuklu?unun Tespit Edilmesi]
    (Institute of Electrical and Electronics Engineers Inc., 2015) Yücelbaş C.; Özşen S.; Yücelbaş S.; Tezel G.; Dursun M.; Yosunkaya S.; Küççüktürk S.
    Sleep staging process that is performed in sleep laboratories in hospitals has an important role in diagnosing some of the sleep disorders and disturbances which are seen in sleep. And also it is an indispensable method. It is usually performed by a sleep expert through examining during the night of the patients (6-8 hours) recorded Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), electrocardiogram (ECG) and other some signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring studies has come to the fore. However, none of the so far made automatic sleep staging was not accepted by the experts. The most important reason is that the results of the automated systems are not desired accuracy. There are many factors that affecting the accuracy of the systems, such as noise, the inter-channel interference, excessive body movements and disconnection of electrodes. In this study, we examined the written an algorithm to be able to determine to what extent the disconnection of electrodes in EEG signal that obtained one healthy person at the sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University. According to the obtained application results, the electrodes disconnection in EEG signal could be detected maximum of 100% and minimum of 99.12% accuracy. Accordingly, based on the success achieved in the study, this algorithm is thought to contribute positively to the researchers that the work on and will work on sleep staging problems and increase the success of automatic sleep staging systems. © 2015 IEEE.
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    Examining the effect of time and frequency domain features of EEG, EOG, and Chin EMG signals on sleep staging [EEG, EOG ve Çene EMG sinyallerinden elde edilen zaman ve frekans domeni özelliklerinin uyku evreleme üzerindeki etkisinin i?ncelenmesi]
    (2010) Özşen S.; Güneş S.; Yosunkaya Ş.
    Sleep staging has an effective role in diagnosing sleep disorders. Sleep staging is generally done by a sleep expert through examining Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections. This type of sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, attention to the automatic sleep staging systems has been begun to increase. In this study, we obtained EEG, EMG and EOG signals of five healthy people in Meram Faculty of medicine to use in sleep staging and extracted 74 features from them. We analyzed the effects of these features on sleep staging. We utilized from the sequential feature selection algorithm and Artificial Neural Networks in this application. We determined which features are more effective in classification of sleep stages and by this way we tried to guide researchers who will use EEG, EMG and EOG features in sleep staging. The highest classification accuracy was obtained as 69.30% with use of four features. ©2010 IEEE.
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    Voxel based morphometric analysis on MR images [MR görüntülerinde voksel tabanli morfometrik analiz]
    (Institute of Electrical and Electronics Engineers Inc., 2017) Öziç M.Ü.; Özşen S.; Ekmekci A.H.
    Examining of progress of diseases that occur in brain using numerical methods is one of the topics of neuroscience researches. One of the developing numerical methods for examinig local and global changes that occur in brain is Voxel Based Morphometry. For examining intragroup and intergroup differences via Voxel Based Morphometry is required to use some preprocessing methods together with statistical tools. In this study, Voxel Based Morphometry method have been examined using labeled Normal Control and Alzheimer Disease. Magnetic Resonanse images were taken from Open Access Series of Imaging Studies database. After examining volumetric differences as statistical that occur in the brain between two groups via Voxel Based Morphometry, it has been mapped on the template atlases. © 2017 IEEE.