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.

Objectives: Besides the well-known sensorimotor control function, the cerebellum is also associated with cognitive functions and mood via the cerebral-cerebellar circuit. This study aimed to investigate possible cerebellar morphometric changes in untreated patients with depression. Methods: Brain magnetic resonance (MR) images of 40 adults (age: 18–50 years), including 20 untreated depression patients and 20 healthy controls were analysed prospectively. Intracranial cavity and total cerebellar volumes were measured by using VolBrain. The cerebellum segmentation was performed with CERES to obtain the total gray matter volumes and cortical thickness of the lobules. Results: Total cerebellar volume was 141.27±13.12 cm3 in the depressed group and 142.63±8.01 cm3 in the control group (p>0.05). The difference between males and females in the depressed group was not statistically significant (p>0.05). Total cerebellar volume was approximately 11% of total intracranial volume in both groups. The cortical thickness of lobule V (right-total), lobule VIIIB (right), and lobule IX (right) was smaller in the depressed group, independent of sex (p<0.05). Lobule V, VIIIB and IX volume was smaller and Crus-I cortical thickness was increased in depressed females (p<0.05). Conclusion: The cerebellar volume and cortical thickness of cerebellar lobules in patients with depression show significant differences compared to healthy subjects.