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Öğe Comparison of Artificial Immune Clustering with Fuzzy C-Means Clustering in the Sleep Stage Classification Problem(2012) Dursun, M.; Güneş, S.; Özşen, S.; Yosunkaya, S.Automatic Sleep Staging is an active field of research in sleep staging community. Many methods have been applied to get rid of the cumbersome of manual staging process. Even though very effective results were taken in some of these methods with respect to the classification accuracy, they are restricted either with their limited classification data or with lower number of classified stages like wake, sleepy and deep sleep. The accuracies obtained with methods for the classification of whole sleep stages are very low to apply in real sleep staging. One reason for this is the class imbalance in training data. Approximately half of one-night sleep consists of Non-REM2 stage while Wake, Non-REM1 and Non-REM3 stages are comparatively short duration. So, the used systems can converge to the characteristics of Non-REM2 stage. Taking equal amounts of data from each stage in training can be a solution for this but in this time a question arises: which samples should be picked from the each stage. Clustering schemes can play their roles for this question. In this study, we realized this clustering process with two methods: Fuzzy C-means Clustering (FCM) and Artificial Immune Clustering (AIC). We used 55 features that extracted from the sleep EEG, EOG and EMG signals of 8 subjects. We took a total of 300 data from each stage using FCM and AIC and classified these data with Artificial Neural Networks. The performances of the used clustering methods were compared on different number of features for which PCA was applied. The results showed that AIC was over-performed to FCM by obtaining a classification accuracy of 80.62% while this accuracy was 72.16% with FCM method used. © 2012 IEEE.Öğe Leptin Levels in Patients with Obstructive Sleep Apnea Syndrome and the Effect of Cpap Treatment Over Leptin Levels(OXFORD UNIV PRESS INC, 2010) Yosunkaya, S.; Doğan Ünüvar, F.; Okur, H.; Özer, F.[Abstract not Available]Öğe Pairwise Classifier Approach to Automated Diagnosis of Disorder Degree of Obstructive Sleep Apnea Syndrome: Combining of AIRS and One versus One (OVO-AIRS)(INT ASSOC ENGINEERS-IAENG, 2009) Polat, K.; Guenes, S.; Yosunkaya, S.Artificial Immune Recognition System (AIRS) is an immune inspired supervised classification algorithm and also works in classifying of multi class datasets. But the performance of AIRS classifier in classifying multi class datasets is generally lower than its performance in case of classifying two class datasets. In order to overcome this problem, we have combined the one-versus-one (OVO) and AIRS in the diagnosis of disorder degree of obstructive sleep apnea syndrome (OSAS) that affects both the right and the left cardiac ventricle. The OSAS dataset consists of four classes including of normal (25 subjects), mild OSAS (AM (Apnea Apnea and Hypoapnea Index)=5-15 and 14 subjects), moderate OSAS (AHI<15-30 and 18 subjects), and serious OSAS (AHI>30 and 26 subjects). In the extracting of features that is characterized the OSAS disease, the clinical features obtained from Polysomnography used diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering from this disease have been used. The used clinical features are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. We have used two fold cross validation to split OSAS dataset and also used the classification accuracy, sensitivity- specificity analysis, and confusion matrix to evaluate the performance of proposed method. While AIRS algorithm obtained 90.24% classification accuracy, the proposed method based on AIRS algorithm and OVO achieved 98.24% classification accuracy. These results show that the proposed method can confidently be used in the determining of disorder degree of OSAS.Öğe TNF-alpha, IL-6 AND NT pro-BNP LEVELS IN PATIENTS WITH OBSTRUCTIVE SLEEP APNEA SYNDROME AND THE EFFECT OF CPAP TREATMENT OVER LEVELS OF THESE PARAMETERS(OXFORD UNIV PRESS INC, 2010) Dogan, Unuvar F.; Yosunkaya, S.; Okur, H.; Ozer, F.[Abstract not Available]Öğe Tolerability of nimesulide in patients with histories of adverse reactions to acetylsalicylicacid and non steroidal antiinflammatory drugs(WILEY-BLACKWELL, 2013) Tepetam, F. M.; Colakoglu, B.; Ozer, F.; Maden, E.; Yosunkaya, S.; Duman, D.; Oruc, O.[Abstract not Available]
