Sen, Sena YagmurAkan, AydinCura, Ozlem Karabiber2024-11-252024-11-252024978946459361797983315197732076-1465https://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/closedAccessAlzheimer'S Dementia (Ad)Electroencephalography (Eeg)Intrinsic Time-Scale Decomposition (Itd)Short-Time Fourier Transform (Stft)Convolutional Neural Network (Cnn)Conference Object10.23919/EUSIPCO63174.2024.107150052-s2.0-85208442090