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    Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods
    (SPRINGER LONDON LTD, 2018) Yucelbas, Cuneyt; Yucelbas, Sule; Ozsen, Seral; Tezel, Gulay; Kuccukturk, Serkan; Yosunkaya, Sebnem
    Sleep staging is a significant process to diagnose sleep disorders. Like other stages, several parameters are required for the determination of N-REM2 stage. Sleep spindles (SSs) are among them. In this study, a methodology was presented to automatically determine starting and ending positions of SSs. To accomplish this, short-time Fourier transform-artificial neural networks (STFT-ANN), empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods were used. Two considerable methods which were determination envelope and thresholding of the decomposed signals by EMD and DWT were also presented in this study. A database from the EEG signals of nine healthy subjects-which consisted of 100 epochs including 172 SSs in total-was prepared. According to the test results, the highest sensitivity rate was obtained as 100 and 99.42 % for EMD and DWT methods. However, the sensitivity rate for the STFT-ANN method was recorded as 55.93 %. The results indicated that the EMD method could be confidently used in the automatic determination of SSs. Thanks to this study, the sleep experts will be able to reliably find out the epochs with SSs and also know the places of SSs in these epochs, automatically. Another important point of the study was that the sleep staging process-tiring, time-consuming and high fallibility for the experts-could be performed in less time and at higher accuracy rates.
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    Detection of the Electrode Disconnection in Sleep Signals
    (IEEE, 2015) Yucelbas, Cuneyt; Ozsen, Seral; Yucelbas, Sule; Tezel, Gulay; Dursun, Mehmet; Yosunkaya, Sebnem; Kuccukturk, Serkan
    Sleep 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.
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    Elimination of EMG Artifacts From EEG Signal in Sleep Staging
    (IEEE, 2016) Ozsen, Seral; Yucelbas, Cuneyt; Yucelbas, Sule; Tezel, Gulay; Yosunkaya, Sebnem; Kuccukturk, Serkan
    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.
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    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, Sebnem
    Interference 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.
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    A novel system for automatic detection of K-complexes in sleep EEG
    (SPRINGER LONDON LTD, 2018) Yucelbas, Cuneyt; Yucelbas, Sule; Ozsen, Seral; Tezel, Gulay; Kuccukturk, Serkan; Yosunkaya, Sebnem
    Sleep staging process is applied to diagnose sleep-related disorders by sleep experts through analyzing sleep signals such as electroencephalogram (EEG), electrooculogram and electromyogram of subjects and determining the stages in 30-s-length time parts of sleep named as epochs. Subjects enter several stages during the sleep, and N-REM2 is one of them which has also the highest duration among the other stages. Approximately half of the sleep consists of N-REM2. One of the important parameters in determining N-REM2 stage is K-complex (Kc). In this study, some time and frequency analysis methods were used to determine the locations of Kcs, automatically. These are singular value decomposition (SVD), variational mode decomposition and discrete wavelet transform. The performance of them in detecting Kcs was compared. Furthermore, systems with combinations of these methods were presented with logic AND operations. The EEG recordings of seven subjects were obtained from the Sleep Research Laboratory of Necmettin Erbakan University. A database with total 359 Kcs in 320 epochs was prepared from the recordings. According to the results, the highest average recognition rate was found as 92.29% for the SVD method. Thanks to this study, the sleep experts can find out whether there were Kcs in related epochs and also know their locations in these epochs, automatically. Also, it will help automatic sleep stage classification systems.
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    Pre-determination of OSA degree using morphological features of the ECG signal
    (PERGAMON-ELSEVIER SCIENCE LTD, 2017) Yucelbas, Sule; Yucelbas, Cuneyt; Tezel, Gulay; Ozsen, Seral; Kuccukturk, Serkan; Yosunkaya, Sebnem
    Obstructive sleep apnea (OSA) is a very common, but a difficult sleep disorder to diagnose. Recurrent obstructions form in the airway during sleep, such that OSA can threaten a breathing capacity of patients. Clinically, continuous positive airway pressure (CPAP) is the most specific and effective treatment for this. In addition, these patients must be separated according to its degree, with CPAP treatment applied as a result. In this study, 30 OSA patients from two different databases were automatically classified using electrocardiogram (ECG) data, identified as mild, moderate, and severe. One of the databases was original recordings which had 9 OSA patients with 8303 epochs and the other one was Physionet benchmark database which had 21 patients with 20,824 epochs. Fifteen morphological features could be identified when apnea was seen, both before and after it presented. Five data groups in total for first dataset and second dataset were prepared with these features and 10-fold cross validation was used to effectively determine the test data. Then, sequential backward feature selection (SBFS) algorithm was applied to understand the more effective features. The prepared data groups were evaluated with artificial neural networks (ANN) to obtain optimum classification performance. All processes were repeated for ten times and error deviation was calculated for the accuracy. Furthermore, different classifiers which are frequently used in the literature were tested with selected features. The degree of OSA was estimated from three epochs in pre-apnea data, yielding the success rates of 97.20 +/- 2.15% and 90.18 +/- 8.11% with the SBFS algorithm for the first and second datasets, respectively. Also, SVM classifier followed ANN system in the success rates of 96.23 +/- 3.48% and 88.75 +/- 8.52% for used datasets. (C) 2017 Elsevier Ltd. All rights reserved.