Sen, Sena YagmurAkan, AydinCura, Ozlem Karabiber2026-03-272026-03-272024-08-26978946459361797983315197732219-54912076-1465https://hdl.handle.net/20.500.14365/8887https://doi.org/10.23919/eusipco63174.2024.10715005https://doi.org/10.23919/EUSIPCO63174.2024.10715005This paper presents a novel early-stage Alzheimer's dementia (AD) disease detection based on convolutional neural networks (CNNs). As it is widely used in detection and classification of AD disease, a time-frequency (TF) method has been proposed for AD detection. It has been described to address the problem of detecting early-stage AD by combining TF and CNN methods. The method is developed by utilizing the well-known structural similarity index measure (SSIM) to obtain discriminative features in each TF image. Experimental results demonstrate that the proposed method outperforms the early-stage AD detection using advanced signal decomposition algorithm that is intrinsic time-scale decomposition (ITD), and it achieves a notable improvement in terms of the detection success rates compared to AD detection from TF images of raw EEG signals.eninfo:eu-repo/semantics/closedAccessShort-Time Fourier Transform (STFT)Convolutional Neural Network (CNN)Electroencephalography (EEG)Intrinsic Time-Scale Decomposition (ITD)Alzheimer’s Dementia (AD)Conference Object10.23919/eusipco63174.2024.107150052-s2.0-85208442090