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While the precise etiology of dementia remains incompletely elucidated, its manifestation is frequently associated with discernible structural and chemical alterations in the brain. Living with dementia significantly impacts individuals\u2019 daily lives due to the resultant loss of cognitive functions. This study presents a novel method to monitor and detect AD using advanced signal processing applied to electroencephalography (EEG) signals. The intrinsic time-scale decomposition (ITD) algorithm is employed to extract proper rotation components (PRCs) from EEG signals, utilizing a 5-second EEG segment duration. The proposed method is compared with the detection of 5-second raw EEG segments using a custom one-dimensional convolutional neural network (1D CNN). Additionally, four different quartiles (Quartile 1 (Q1), Q2, Q3, and Q4) of EEG signals are considered to identify the most significant contributor to AD. Experimental results demonstrate that the ITD-based approach yields better detection performance compared to using raw EEG signals. The most promising result is achieved by the EEG-PRCs method in Q1, with an accuracy of 94.00%, sensitivity of 93.50%, and specificity of 93.90%. In contrast, the highest-performing result of the raw EEG segments method is in Q2, with an accuracy of 88.40%, sensitivity of 89.10%, and specificity of 87.60% in terms of detecting AD.<\/jats:p>","DOI":"10.1177\/01423312241267046","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T02:08:10Z","timestamp":1723601290000},"page":"1353-1365","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Classification of Alzheimer\u2019s dementia EEG signals using deep learning"],"prefix":"10.1177","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0667-9603","authenticated-orcid":false,"given":"Sena Yagmur","family":"Sen","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Izmir University of Economics, Turkiye"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8650-1137","authenticated-orcid":false,"given":"Ozlem Karabiber","family":"Cura","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Izmir Katip Celebi University, Turkiye"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7873-0383","authenticated-orcid":false,"given":"Gulce Cosku","family":"Yilmaz","sequence":"additional","affiliation":[{"name":"Department of Neurology, Izmir University of Economics, MedicalPoint International Hospital, Turkiye"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8894-5794","authenticated-orcid":false,"given":"Aydin","family":"Akan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Izmir University of Economics, Turkiye"}]}],"member":"179","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSP53291.2023.10208023"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.13016"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/geronb\/gbz127"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.3389\/frdem.2023.1346281"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103514"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1049\/icp.2023.2901"},{"key":"e_1_3_2_8_1","first-page":"141","article-title":"Deep learning-based classification of multi-categorical Alzheimer\u2019s disease data","volume":"10","author":"Cohen D","year":"2019","unstructured":"Cohen D, Carpenter K, Jarrell J, et al. 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