Alzheimer's Dementia Detection: an Optimized Approach Using Itd of Eeg Signals

Abstract

This 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.