Yazar "Ozsen S." seçeneğine göre listele

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    Comparison of some spectral analysis methods in detection of sleep spindles using YSA [Uyku I?ci?i Tespitinde Bazi Spektral Analiz Yöntemlerinin YSA kullanilarak Karşilaştirilmasi]
    (Institute of Electrical and Electronics Engineers Inc., 2015) Ozsen S.; Dursun M.; Yosunkaya S.
    Sleep 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%. © 2015 IEEE.
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    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.
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    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.
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    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.
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    Effect of some power spectral density estimation methods on automatic sleep stage scoring using artificial neural networks
    (2013) Yucelbas C.; Ozsen S.; Gunes S.; Yosunkaya S.
    Sleep staging has an important role in diagnosing sleep disorders. It is usually done by a sleep expert through examining sleep Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) 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 systems get popularity. In this study, we obtained EEG, EMG and EOG signals of four healthy people at sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University to use them in sleep staging and extracted 20 different features by using some power spectral density estimation methods which are: Fast Fourier Transform (FFT), Welch and Autoregressive (AR). We evaluated the effects of these methods on sleep staging through using ANN classifier. Comparison between these methods was done on each individual whose data were utilized separately from others. According to the results, mean of test classification accuracies for all of subjects were obtained as 74.14%, 71,57 and 70.34% with use of FFT, Welch and AR, respectively. © 2013 IADIS.
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    Effect of the Hilbert-Huang transform method on sleep staging [Hilbert-Huang Dönüşümü Metodunun Uyku Evreleme Üzerindeki Etkisi]
    (Institute of Electrical and Electronics Engineers Inc., 2017) Yucelbas C.; Yucelbas S.; Ozsen S.; Tezel G.; Yosunkaya S.
    Sleep scoring is performed by examining the recorded electroencephalogram (EEG) and some other signals recorded by a polysomnography (PSG) device. This process is considered more reliable as it is done manually by experts. However, due to the fact that experts may also be mistaken, it has led to an increase in the importance given to automatic sleep staging studies. Many methods have been tested on the signals in order to increase the success of these systems. In this study, an automatic sleep staging system was implemented using the Hilbert-Huang transformation method which is a new time-frequency analysis type. In the study, EEG signals from 5 subjects were used in the sleep laboratory. In the 5-class (Alpha, Beta, Theta, Delta and Spindle bands) applications, the highest classification success was 84.75% as a result of sequential feature selection method. © 2017 IEEE.
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    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.