Enhancing Alzheimer's Detection through Late Fusion of Multi-Modal EEG Features

TL;DR

This paper introduces a deep learning framework that combines multiple EEG feature extraction methods through late fusion to improve early Alzheimer's detection accuracy.

Contribution

It presents a novel multi-modal EEG analysis approach with late fusion of neural networks, achieving high classification performance for AD diagnosis.

Findings

Achieved 87.23% classification accuracy on a public dataset.

Late fusion of diverse EEG features enhances diagnostic reliability.

Targeted alpha-band analysis improves model performance.

Abstract

Alzheimer s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, where early detection is essential for timely intervention and improved patient outcomes. Traditional diagnostic methods are time-consuming and require expert interpretation, thus, automated approaches are highly desirable. This study presents a novel deep learning framework for AD diagnosis using Electroencephalograph (EEG) signals, integrating multiple feature extraction techniques including alpha-wave analysis, Discrete Wavelet Transform (DWT), and Markov Transition Fields (MTF). A late-fusion strategy is employed to combine predictions from separate neural networks trained on these diverse representations, capturing both temporal and frequency-domain patterns in the EEG data. The proposed model attains a classification accuracy of 87.23%, with a precision of 87.95%, a recall of 86.91%, and an F1 score of 87.42% when evaluated on a publicly available dataset, demonstrating its potential for reliable, scalable, and early AD screening. Rigorous preprocessing and targeted frequency band selection, particularly in the alpha range due to its cognitive relevance, further enhance performance. This work highlights the promise of deep learning in supporting physicians with efficient and accessible tools for early AD diagnosis.