A novel approach hybrid of ensemble learning and 3-D CNN mechanism: early-stage diagnosis of Alzheimer's disease using EEG signals.

Alzheimer's disease (AD) is a progressive neurological disorder that causes brain cell degeneration and leads to dementia. Early and accurate detection of AD is crucial, as it allows timely treatment before the brain suffers permanent damage. In recent years, computer-aided methods using Artificial Intelligence (AI) have shown promise in improving the diagnosis of AD, healthy cognition (HC), and other types of dementia. However, distinguishing between AD and HC using Electroencephalography (EEG) signals remains challenging, mainly due to difficulties in identifying meaningful features from the signals. To address this issue, we propose a novel method called EDL3DCNN, which combines Ensemble Deep Learning (EDL) with a 3D Convolutional Neural Network (3D-CNN). This model is designed to diagnose and classify AD and HC subjects accurately. We trained and evaluated the model using two publicly available EEG datasets related to AD. The EDL3DCNN model, leveraging multiple 3D-CNN classifiers, achieved a high classification accuracy of 99.02%. Our results demonstrate that integrating EDL with 3D-CNN offers a robust and scalable solution for computer-aided AD diagnosis.