Deep generative adversarial networks with marine predators algorithm for classification of Alzheimer’s disease using electroencephalogram

Abstract

Alzheimer's disease (AD) is a neurological disorder characterized by cognitive decline and memory loss. An early and precise diagnosis of Alzheimer's disease is critical for effective therapy and management. The electroencephalogram (EEG) has shown promise as a non-invasive and cost-effective tool for Alzheimer's disease (AD) categorization. The capacity to diagnose Alzheimer's disease at an early stage is one of the benefits of EEG (electroencephalogram) over other methodologies in Alzheimer's disease research. Traditional EEG analysis approaches, such as estimating coherence between various pairs of electrodes, necessitate a significant amount of human labor. We introduce a novel strategy for AD classification based on EEG by combining deep generative adversarial networks (GANs) and the Marine Predators Algorithm (MPA). GANs are powerful deep-learning models that can be trained to generate realistic samples via negative generator and discriminator training. MPA is a nature-inspired optimization method well-known for its ability to solve complicated optimization problems. The proposed system generates synthetic EEG samples using GANs' ability to learn meaningful representations from raw EEG data. The MPA is then employed to enhance classification performance by optimizing the discriminative features extracted by GANs. The MPA simulates the hunting behavior of marine predators, facilitating the exploration of the feature space and identifying significant characteristics for AD categorization. We evaluated the efficacy of the proposed strategy using a publicly accessible EEG database of AD patients and healthy controls. The results of classification precision, sensitivity, and specificity demonstrate that deep GANs with MPA are superior to state-of-the-art techniques.