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.

European Journal of PainVolume 10, Issue S1 p. S189b-S189 725 COMPARISON OF SUFENTANIL AND FENTANYL BY PATIENT-CONTROLLED ANALGESIA FOR POSTOPERATIVE ANALGESIA AFTER ABDOMINAL HYSTERECTOMY S. Tuncer, S. Tuncer Selcuk University Meram Medical Faculty Department of Algology, KonyaSearch for more papers by this authorM. Dursun, M. Dursun Selcuk University Meram Medical Faculty Department of Anaesthesiology, Konya, TurkeySearch for more papers by this authorR. Reisli, R. Reisli Selcuk University Meram Medical Faculty Department of Algology, KonyaSearch for more papers by this authorS. Otelcioǧlu, S. Otelcioǧlu Selcuk University Meram Medical Faculty Department of Anaesthesiology, Konya, TurkeySearch for more papers by this author S. Tuncer, S. Tuncer Selcuk University Meram Medical Faculty Department of Algology, KonyaSearch for more papers by this authorM. Dursun, M. Dursun Selcuk University Meram Medical Faculty Department of Anaesthesiology, Konya, TurkeySearch for more papers by this authorR. Reisli, R. Reisli Selcuk University Meram Medical Faculty Department of Algology, KonyaSearch for more papers by this authorS. Otelcioǧlu, S. Otelcioǧlu Selcuk University Meram Medical Faculty Department of Anaesthesiology, Konya, TurkeySearch for more papers by this author First published: 13 January 2012 https://doi.org/10.1016/S1090-3801(06)60728-3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume10, IssueS1September 2006Pages S189b-S189 RelatedInformation

Sleep scoring is a time consuming and difficult task conducted by sleep specialist. The main aims of this study are to propose a novel data pre-processing method called K-means clustering based feature weighting (KMCFW) and to develop an automatic recognition system for determining of sleep stages. In this paper, we have used a three stage hybrid system comprising: (i) feature extraction using Welch method from PSG (Polysomnogram) signals including EEG (Electroencephalogram) and chin EMG (Electromyogram), (ii) data pre-processing using KMCFW, and (iii) sleep stages classifying using C4.5 decision tree classifier. There are five sleep stages: Awake, REM (Rapid Eye Movement), N-REM (Non-Rapid Eye Movement) stage 1, N-REM stage 2, and N-REM stage 3. In order to determine the sleep stages, three all night PSG recordings were used for this study. Using alone spectral features belonging to EEG signal; decision tree has obtained classification accuracy of 37.84% on classification of sleep stages using ten fold cross validation. Sleep stages have been classified with accuracy of 41.85% using decision tree based on spectral features belonging to EEG and chin EMG signals. In weighted spectral features belonging to EEG signal with KMCFW, the classification accuracy of 92.40% has been achieved on sleep stages classification using decision tree. As for weighted spectral features belonging to EEG and chin EMG signals with KMCFW, sleep stages has been determined with accuracy of 93.39% using decision tree. These results have demonstrated that the proposed weighting method have a considerable impact on determining of sleep stages.