Cura, Ozlem KarabiberYilmaz, Gulce CoskuTure, Hatice SabihaAkan, Aydin2023-06-162023-06-162021978-1-6654-3663-2https://doi.org/10.1109/TIPTEKNO53239.2021.9633007https://hdl.handle.net/20.500.14365/1987Medical Technologies Congress (TIPTEKNO'21) -- NOV 04-06, 2021 -- Antalya, TURKEYNeurological disorders may spring from any disorder in the brain or the central and autonomic nervous systems. Among the neurological disorders, while Alzheimer's disease and other dementias are the fourth-largest contributors of disabilityadjusted life years, they are the second largest contributor of deaths. In the proposed study, various signal decomposition methods such as EMD, EEMD, and DWT are presented to classify EEG segments of control subjects and Alzheimer' dementia patients. Time-domain features are calculated using selected 7 IMFs and 5 detail and approximation coefficients of DWT. Various classification techniques namely Decision Tree (DT), Support Vector Machine (SVM), k- Nearest Neighbor (kNN), and Random Forest (RF) are utilized to distinguish two groups. Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates.eninfo:eu-repo/semantics/closedAccessAlzheimer' dementiaEmpirical ModeDecompositionEnsemble Empirical Mode DecompositionDiscrete Wavelet TransformEEG classification.Eeg Background ActivityPermutation EntropyDisease PatientsComplexityConnectivityConference Object10.1109/TIPTEKNO53239.2021.96330072-s2.0-85123715545