Browsing by Author "Ozcoban, Mehmet Akif"

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Citation - WoS: 3
Citation - Scopus: 2
An Eeg and Machine Learning Based Method for the Detection of Major Depressive Disorder
(IEEE, 2021-06-09) Izci, Elif; Ozdemir, Mehmet Akif; Akan, Aydin; Ozcoban, Mehmet Akif; Arikan, Mehmet Kemal
Major depressive disorder (MDD) is a common mood disorder encountered worldwide. Early diagnosis has great importance to prevent the negative effects on the person. The aim of this study is to develop an objective method to differentiate MDD patients from healthy controls. Electroencephalography (EEG) signals taken from 16 MDD patients and 16 healthy subjects are analyzed according to the regions of the brain, and time-domain, frequency-domain, and nonlinear features were extracted. The feature sets are classified using five different classification algorithms. As a result of the study, a classification accuracy of 89.5% was yielded using the Bagging classifier with 7 features calculated from the central EEG channels.
Citation - WoS: 9
Citation - Scopus: 9
Intrinsic Synchronization Analysis of Brain Activity in Obsessive-Compulsive Disorders
(World Scientific Publ Co Pte Ltd, 2020-08-19) Ozel, Pinar; Karaca, Ali; Olamat, Ali; Akan, Aydin; Ozcoban, Mehmet Akif; Tan, Oguz
Obsessive-compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample t-test and U-test has shown the significance of such new methodology.