DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features

TL;DR

DeepTokenEEG is a lightweight, high-performance model that improves Alzheimer's detection accuracy using tokenized EEG features, demonstrating potential for early diagnosis with a compact design.

Contribution

Introduces DeepTokenEEG, a novel lightweight model utilizing spatial and temporal tokenization to effectively classify EEG signals for Alzheimer's detection.

Findings

Achieved up to 100% accuracy on specific frequency bands.

Outperformed state-of-the-art methods by 1.41-15.35%.

Utilized only 0.29 million parameters, enabling deployment.

Abstract

The detection of Alzheimers disease (AD) is considered crucial, as timely intervention can improve patient outcomes. Electroencephalogram (EEG)-based diagnosis has been recognized as a non-invasive, accessible, and cost-effective approach for AD detection; however, it faces challenges related to data availability, accuracy of modern deep learning methods, and the time-consuming nature of expert-based interpretation. In this study, a novel lightweight and high-performance model, DeepTokenEEG, was designed for the diagnosis of AD and the classification of EEG signals from AD patients, individuals with other neurological conditions, and healthy subjects. Unlike traditional heavy-weight models, DeepTokenEEG ultilizes spatial and temporal tokenizer that effectively captures AD-related biomarkers in both temporal and frequency domain with only 0.29 million paramaters. Trained in a combined dataset of 274 subjects, including 180 AD cases, and 94 healthy controls, the proposed method achieves a maximum recorded accuracy of 100% on specific frequency bands, representing an improvement of 1.41-15.35% over state-of-the-art methods on the same dataset. These results indicate the potential of DeepTokenEEG for early detection and screening of AD, with promising applicability for deployment due to its compact size.