Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state.

ABSTRACT:

Introduction

Diagnosing Alzheimer's disease (AD) lesions via visual examination of Electroencephalography (EEG) signals poses a considerable challenge. This has prompted the exploration of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), for AD prediction. However, the classification performance of CNN-based methods has often been deemed inadequate. This is primarily attributed to CNNs struggling with extracting meaningful lesion signals from the complex and noisy EEG data.

Methods

In contrast, ViTs have demonstrated proficiency in capturing global signal patterns. In light of these observations, we propose a novel approach to enhance AD risk assessment. Our proposition involves a hybrid architecture, merging the strength

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10563817 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature