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Öğe Detection of REM in sleep EOG signals(Indian Society for Education and Environment, 2016) Coskun A.; Ozsen S.; Yucelbas S.; Yucelbas C.; Tezel G.; Kuccukturk S.; Yosunkaya S.Background/Objectives: Sleep staging is very important phase for diagnosing respiration and sleep diseases. Nowadays, Electroencephalogram (EEG), Electromyogram (EMG), Electrooculogram (EOG) signals are particularly used together in studies on sleep staging. Methods/Statistical Analysis: Associating only EOG signals to sleep staging was distinctly purposed. So, this paper deals with extraction features and classifying for determining REM-NREM states from the EOG signals. In this study, left eye (LEOG) and the right eye (REOG) signals were used. After EOG signals were obtained, 21 different features were extracted from LEOG and REOG in time and frequency domain according to rules of American Academy of Sleep Medicine (AASM). Findings: Artificial Neural Networks (ANN) was adopted on features as method of classification with 3-fold cross validation technique and reached conclusion with the maximum test classification accuracy as 88.05%. To obtain higher classification accuracies, Sequential Backward Selection (SBS) method was used. According to results of SBS, number of the best features combination was determined as 13 and the maximum classification accuracy was obtained as 89.62%. The optimum value of hidden layer node number of ANN was determined as 15 for the best features. Application/Improvements: When looking from the viewpoint of percentage of classification accuracy of this study, a result can be seen that is non-negligible value for literature.Öğe Detection of sleep spindles in sleep EEG by using the PSD methods(Indian Society for Education and Environment, 2016) Yucelbas C.; Yucelbas S.; Ozsen S.; Tezel G.; Kuccukturk S.; Yosunkaya S.Background/Objectives: In this study, Fast Fourier Transform (FFT), Welch, Autoregressive (AR) and MUSIC methods were implemented to detect sleep spindles (SSs) in Electroencephalogram (EEG) signals by extracting features in frequency space. Methods/Statistical Analysis: A database from these signals of five subjects which were recorded at sleep laboratory of Necmettin Erbakan University in Turkey was ready for use. The database consisted of 600 EEG epochs in total. The number of epochs was 300 for both with and without SSs in this database. Comparison of the performances of these methods on SS determination process was performed by using Artificial Neural Networks (ANN) classifier. Findings: According to the test classification results, notable difference was obtained between the applied PSD methods. By using the extracted all features, maximum test classification accuracies were achieved as 84.83%, 80.67%, 80.83% and 80.33% with use of FFT, Welch, AR and MUSIC, respectively. To determine the SSs, Principal Component Analysis (PCA) also was utilized in this study. When PCA was applied, the results were 89.50%, 82.00%, 93.00% and 94.83% by use of the same PSD methods, respectively. Application/Improvements: As a result, the performance of PCA and MUSIC is better than the others. Hence, these methods can be used safely for automatic detection of SSs.Öğe Effect of EEG time domain features on the classification of sleep stages(Indian Society for Education and Environment, 2016) Yucelbas S.; Ozsen S.; Yucelbas C.; Tezel G.; Kuccukturk S.; Yosunkaya S.Background/Objectives: Studies on the field of automatic sleep stage classification have been taking more attention of researchers day by day. Noise in the recordings, nonlinear dynamic feature of EEG signals and some other reasons affect the performance of proposed systems in negative manner. Methods/Statistical Analysis: Sleep can be divided five main stages as Wake, Non-REM1, Non-REM2, Non-REM3 and REM. Almost every proposed method can successfully classify some evident stages like Non-REM2 and REM. But when it comes to the transitions between stages, the systems are not very good in their performances. Thus a different classification strategy was proposed in this study. Five different classifiers were designed especially for transitions between stages using time domain features of EEG, EOG and EMG signals and evaluated these features for each classifier. Sequential backward feature selection process was applied in each classifier to find out which features are dominant in each classification procedure. Artificial Neural Networks was used in designed classifiers. Findings: The highest classification accuracy was obtained as 91.03% for Classifier-3 which predicts stages coming after Non-REM II. The lowest accuracy was recorded as 75.42% for Classifier-2 in which stages are determined after the Non-REM I epochs. Comparatively good results were reached especially if it is taken into account that only used time-domain features of signals. Application/Improvements: The obtain results show that the designed classifiers can be used in automatic sleep staging system, confidently.Öğe Elimination of EMG artifacts from EEG signal in sleep staging [Uyku evrelemede EMG Gürültülerinin EEG Sinyalinden Elenmesi](Institute of Electrical and Electronics Engineers Inc., 2016) Ozsen S.; Yucelbas C.; Yucelbas S.; Tezel G.; Yosunkaya S.; Kuccukturk S.Sleep staging is a tiring and time-consuming process for the experts. Thus, attention given to automatic sleep staging studies is increasing gradually. Many factors such as effects of EOG and EKG signals to EEG result in contaminated signals rather than clear recorded signals. EMG contamination to EEG is among that kind of factors. In this study, some filters and Discrete Wavelet Transform based EMG artifact elimination process were evaluated on the performance of sleep staging process. Features were extracted from cleaned EEG signals and subjected to a classifier to conduct sleep staging. By using test classification accuracy as a measure of performance, the method giving highest accuracy was tried to be found. Artificial Neural Networks was used in the applications and Discrete Wavelet Transform was found to be the method giving the highest accuracy. © 2016 IEEE.
