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Öğe Comparison of Some Spectral Analysis Methods in Detection of Sleep Spindles Using YSA(IEEE, 2015) Ozsen, Seral; Dursun, Mehmet; Yosunkaya, SebnemSleep spindle is a very determinant factor for detection of Non-REM2 stage in sleep staging studies. When it is considered that about half of the sleep consists of Non-REM2 stage, the importance of automatic sleep spindle detection stands out. In this study, three different spectral analysis method- FFT, Welch and AR have been used to estimate the frequency spectrum of sleep EEG signal and feature extraction from this spectrum has been realized. Obtained features have been used in ANN to classify EEG epochs as epochs with spindle and epochs without spindle. It has been observed that least classification error was obtained with FFT as 15.16%.Öğe Detection of the Electrode Disconnection in Sleep Signals(IEEE, 2015) Yucelbas, Cuneyt; Ozsen, Seral; Yucelbas, Sule; Tezel, Gulay; Dursun, Mehmet; Yosunkaya, Sebnem; Kuccukturk, SerkanSleep 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 (FOG), 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.Öğe EEG sinyallerinde uyku iğciklerinin zaman ve frekans domeni özellikleri kullanılarak analizi(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2009) Dursun, Mehmet; Güneş, SalihBu tez çalışmasında, Elektroansefalogram (EEG) kayıtlarının otomatik olarak değerlendirilip, uyku evreleri içinde N-REM (Hızlı Göz Hareketleri Olmayan) Evre 2' nin bulunmasında önemli bir bileşen olan uyku iğciklerinin bulunması amaçlanmıştır. Bu çalışmada, önerilen metot iki adım içermektedir. İlk adımda ham EEG sinyallerinden zaman domeninde altı ve frekans domeninde dört özellik çıkarılmıştır. Ham EEG sinyallerinden Frekans domeni özelliklerinin çıkarılmasında Welch Spectral Analiz yöntemi kullanılmıştır. İkinci adımda ise yapay sinir ağları ile Levenberg-Marquardt algoritması kullanılarak daha önceden uyku uzmanı tarafından elle değerlendirilen uyku iğcikleri sınıflandırılmıştır. Uyku iğciklerinin sınıflandırılmasında LM-YSA kullanılarak elde edilen sınıflama doğruluğu zaman domeni özellikleri için % 100' dür (Gizli katmandaki düğüm sayısı on için). Çalışmada kullanılan uyku EEG sinyalleri Selçuk Üniversitesi Meram Tıp Fakültesi Uyku Laboratuvarında yapılan uyku skorlandırma çalışmaları dâhilinde, Yrd. Doç. Dr. Şebnem Yosunkaya ve ekibi tarafından kaydedilmiştir. Çalışma 3 hastada, toplam 712 epok üzerinde test edilmiştir. Son olarak; elde edilen sonuçlar, önerilen tanıma sisteminin uyku iğciklerinin otomatik olarak bulunmasında güvenle kullanılabileceğini göstermiştir. Aynı zamanda bu sistem N-REM evre 2' nin belirlenmesinde çok önemli bir parametredir.Öğe EEG, EOG ve Çene EMG Sinyallerinin Zaman Domeni Özelliklerinin Uyku Evreleri ile İlişkisinin İncelenmesi(2009) Güneş, Salih; Polat, Kemal; Dursun, Mehmet; Yosunkaya, ŞebnemSleep staging has an important role in determining sleep disorders such as sleepiness, human fatigue etc. Sleep staging is generally done according to Rechtschaffen and Kales standard (RKS) using EEG signal obtained from PSG signals taken from patient subjects who come with any sleep disorders. Sleep stages are generally divided into three stages including awake, REM and N-REM (stage 1, stage 2, and stage 3). In this study, time domain features of EEG, EOG of right and left eyes, and chin EMG signals belonging to sleep stages were investigated and correlation between these time domain features and sleep stages was calculated. The used time domain features are mean value, standard deviation, peak value, skewness, kurtosis, and shape factor belonging to EEG, EOG of right and left eyes, and chin EMG signals. In experimental studies, PSG recordings of 3 subjects were taken and average recording time of 6.22 h, total recording time was 18.67 h. When investigated correlation coefficients, it is seen that skewness feature in time domain features of EEG signal, standard deviation feature in time domain features of EOG signals belonging to right and left eyes, and mean value feature in time domain features of chin EMG signal were more correlated with sleep stages than other features. Consequently, a feature vector can be constituted combining features determined from time domain features of EEG, EOG belonging to right and left eyes, and chin EMG signals. This obtained feature vector can be easily used in distinguishing sleep stages.Öğe Examining the Relevance with Sleep Stages of Time Domain Features of EEG, EOG, and Chin EMG signals(IEEE, 2009) Gunes, Salih; Polat, Kemal; Dursun, Mehmet; Yosunkaya, SebnemSleep staging has an important role in determining sleep disorders such as sleepiness, human fatigue etc. Sleep staging is generally done according to Rechtschaffen and Kales standard (RKS) using EEG signal obtained from PSG signals taken from patient subjects who come with any sleep disorders. Sleep stages are generally divided into three stages including awake, REM and N-REM (stage 1, stage 2, and stage 3). In this study, time domain features of EEG, EOG of right and left eyes, and chin EMG signals belonging to sleep stages were investigated and correlation between these time domain features and sleep stages was calculated. The used time domain features are mean value, standard deviation, peak value, skewness, kurtosis, and shape factor belonging to EEG, EOG of right and left eyes, and chin EMG signals. In experimental studies, PSG recordings of 3 subjects were taken and average recording time of 6.22 h, total recording time was 18.67 h. When investigated correlation coefficients, it is seen that skewness feature in time domain features of EEG signal, standard deviation feature in time domain features of EOG signals belonging to right and left eyes, and mean value feature in time domain features of chin EMG signal were more correlated with sleep stages than other features. Consequently, a feature vector can be constituted combining features determined from time domain features of EEG, EOG belonging to right and left eyes, and chin EMG signals. This obtained feature vector can be easily used in distinguishing sleep stages.Öğe A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification(SPRINGER, 2017) Dursun, Mehmet; Ozsen, Seral; Yucelbas, Cuneyt; Yucelbas, Sule; Tezel, Gulay; Kuccukturk, Serkan; Yosunkaya, SebnemInterference between EEG and EOG signals has been studied heavily in clinical EEG signal processing applications. But, in automatic sleep stage classification studies these effects are generally ignored. Thus, the objective of this study was to eliminate EOG artifacts from the EEG signals and to see the effects of this process. We proposed a new scheme in which EOG artifacts are separated from electrode or other line artifacts by a correlation and discrete wavelet transform-based rule. Also, to discriminate the situation of EEG contamination to EOG from EOG contamination to EEG, we introduced another rule and integrated this rule to our proposed method. The proposed method was also evaluated under two different circumstances: EOG-EEG elimination along the whole 0.3-35 Hz power spectrum and EOG-EEG elimination with discrete wavelet transform in 0-4 Hz frequency range. To see the consequences of EOG-EEG elimination in these circumstances, we classified pure EEG and artifact-eliminated EEG signals for each situation with artificial neural networks. The results on 11 subjects showed that pure EEG signals gave a mean classification accuracy of 60.12 %. The proposed EOG elimination process performed in 0-35 Hz frequency range resulted in a classification accuracy of 63.75 %. Furthermore, conducting EOG elimination process by using 0-4 Hz DWT detail coefficients caused this accuracy to be raised to 68.15 %. By comparing the results obtained from all applications, we concluded that an improvement about 8.03 % in classification accuracy with regard to the uncleaned EEG signals was achieved.Öğe Sleep spindles recognition system based on time and frequency domain features(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Gunes, Salih; Dursun, Mehmet; Polat, Kemal; Yosunkaya, SebnemSleep spindle is the one of important components determining N-REM (Non-Rapid Eye Movement) stage 2 in the sleep stages. The symptoms of N-REM stage 2 are sleep spindle and K-complex and here sleep spindles are automatically recognized by using time and frequency domain features belonging to EEG (Electroencephalograph) signals obtained from three patient subjects. In this study, the proposed method consists of two steps. In the first step, six time domain features have been extracted from raw EEG signals. As for the extraction of frequency domain features from raw EEG signals, Welch spectral analysis has been used and applied to raw EEG signals. By this way, 65 frequency domain features have been extracted and reduced from 65 to 4 features by using statistical measures including minimum, maximum, standard deviation, and mean values. Three feature sets including only time domain, only frequency domain, and both time and frequency domain features have been used and the numbers of these feature sets are 6, 4, and 10, respectively. In the second step, artificial neural network (ANN) with LM (Levenberg-Marquardt) has been used to classify the sleep spindles evaluated beforehand by sleep expert physicians. The obtained classification accuracies for three features sets in the classification of sleep spindles are 100%, 56.86%, and 93.84% by using LM-ANN (for ten node in hidden layer). The obtained results have presented that the proposed recognition system could be confidently used in the automatic classification of sleep spindles. (C) 2010 Elsevier Ltd. All rights reserved.
