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https://hdl.handle.net/20.500.14365/1987
Title: | Classification of Alzheimers' Dementia by Using Various Signal Decomposition Methods | Authors: | Cura, Ozlem Karabiber Yilmaz, Gulce Cosku Ture, Hatice Sabiha Akan, Aydin |
Keywords: | Alzheimer' dementia Empirical ModeDecomposition Ensemble Empirical Mode Decomposition Discrete Wavelet Transform EEG classification. Eeg Background Activity Permutation Entropy Disease Patients Complexity Connectivity |
Publisher: | IEEE | Abstract: | Neurological 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. | Description: | Medical Technologies Congress (TIPTEKNO'21) -- NOV 04-06, 2021 -- Antalya, TURKEY | URI: | https://doi.org/10.1109/TIPTEKNO53239.2021.9633007 https://hdl.handle.net/20.500.14365/1987 |
ISBN: | 978-1-6654-3663-2 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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