Abstract
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.
Highlights
Sleep staging is the most important process in the detection of sleep diseases
Statistical analysis of EOG artifact elimination from the EEG signals The first part of the study involves the elimination of EOG artifacts from the measured EEG signals
In the time domain signals of left and right eye EOGs, measured EEG and estimated (EOG-artifact-eliminated) EEG are given in addition to their fast fourier transform (FFT) curves
Summary
Sleep staging is the most important process in the detection of sleep diseases. The correct diagnosis and appropriate treatment of sleep disorders improve people’s quality of life, and allow them to live safer lives. Automatic sleep staging has been an active field of research area recently. Because the manual sleep staging process is time-consuming and a difficult task undertaken by sleep experts, some more efficient methods have been developed and applied partly to sleep stage classification. Many studies have emphasized that especially nonlinear methods include effective tools to understand the complexity of Electroencephalogram (EEG) signals [1,2,3,4,5,6]. Different nonlinear algorithms might be used in the analysis of sleep EEG signals [7]. Many factors can be pointed here which degrade classification
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