Yazar "Dursun, Mehmet" seçeneğine göre listele

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    Automated elimination of EOG artifacts in sleep EEG using regression method
    (Tubitak Scientific & Technological Research Council Turkey, 2019) Dursun, Mehmet; Ozsen, Seral; Gunes, Salih; Akdemir, Bayram; Yosunkaya, Sebnem
    Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%-1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.
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    Comparison of Some Spectral Analysis Methods in Detection of Sleep Spindles Using YSA
    (IEEE, 2015) Ozsen, Seral; Dursun, Mehmet; Yosunkaya, Sebnem
    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%.
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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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    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.