Cura O.K.Yilmaz G.C.Ture H.S.Akan A.2023-06-162023-06-1620229.78E+12https://doi.org/10.1109/SIU55565.2022.9864898https://hdl.handle.net/20.500.14365/362430th Signal Processing and Communications Applications Conference, SIU 2022 -- 15 May 2022 through 18 May 2022 -- 182415Alzheimer's dementia is a highly prevalent disorder among all neurological disorders. In this study, a new method based on time-Frequency (TF) representations such as Short Time Fourier Transform (STFT) and Synchrosqueezing Transform (SST) is proposed to classify EEG segments of AD patients and control subjects. Previous studies have shown that there are distinctive differences in the EEG signals of control subjects and AD patients in the low-frequency EEG subbands. Hence, in the proposed method TF representations of all EEG subbands are used for feature calculation separately. TF energy distributions obtained by SST and STFT approaches are used to calculate 13 TF features to gather distinctive information between EEG segments of control subjects and AD patients. Various classification techniques are utilized to distinguish feature sets of two the groups. Simulation results demonstrate that the proposed method achieve outstanding validation accuracy rates. © 2022 IEEE.trinfo:eu-repo/semantics/closedAccessAlzheimer's dementiaEEG classificationShort Time Fourier TransformSynchrosqueezing Transformtime-Frequency methodAlzheimer dementiaControl subjectEEG classificationShort time Fourier transformsSubbandsSynchrosqueezingSynchrosqueezing transformTime-frequency approachTime-frequency methodsTime-frequency representationsNeurodegenerative diseasesConference Object10.1109/SIU55565.2022.98648982-s2.0-85138673841