A Unique EEG-Hyfusion Fully Automated Stacked Model for Classification of Alzheimer's Disease and Fronto-Temporal Dementia

Alzheimer's disease (AD) and fronto-temporal dementia (FTD) are common neurodegenerative disorders that impair memory, cognitive function, and executive processing. The purpose of this study is to develop a fully automated machine learning pipeline for predicting Alzheimer's disease at an early stage without requiring any clinical intervention. This methodology proposes a quantitative analysis of subtle neuro-activity shifts. The goal is to develop a reliable, fully automated system that utilizes EEG data to classify patients into AD, FTD, and Healthy Control groups, eliminating the need for human intervention or clinical assessments. A major innovation of the system lies in its signal processing approach and automated feature pipeline. Specifically, the strategic modification of the Nyquist frequency is used to enhance EEG signal resolution, in combination with a hybrid fusion layer that integrates multi-domain EEG features and demographic data. Subsequently, a two-way ANOVA-based feature selection refines this hybrid set. This enhancement facilitates more effective feature extraction, contributing to higher classification accuracy. In the proposed method, frequencies are epoched to enrich the training dataset. And thereby the standard random forest model gives 99.72% training accuracy. To ensure the robustness and generalizability of the method, a hybrid fusion model is proposed.