EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques.

ABSTRACT:

Introduction

Despite the existence of numerous clinical techniques for identifying neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows great promise as a means of detecting Alzheimer's disease (AD) at an early stage. The main goal of this research is to create a reliable and accurate clinical decision support system leveraging EEG signal processing to detect AD in its initial phases.

Methods

The research utilized a dataset consisting of 35 neurotypical individuals, 31 patients with mild AD, and 22 patients with moderate AD. Data were collected while participants were at rest. To extract features from the EEG signals, a band-pass filter was applied to the dataset and the Empirical Mode Decomposition (EMD) technique was employed to decompo

SUBMITTER: AlSharabi K 

PROVIDER: S-EPMC10501399 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature