AccScience Publishing / JBM / Online First / DOI: 10.14440/jbm.2025.0069
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RESEARCH ARTICLE

Electroencephalogram-based time-frequency analysis for Alzheimer’s disease detection using machine learning

Sérgio Daniel Rodrigues1 ,  Pedro Miguel Rodrigues1*
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1 Centre for Biotechnology and Fine Chemistry- Associated Laboratory, Faculty of Biotechnology, Catholic University of Portugal, Rua Diogo Botelho 1327, Porto 4169-005, Portugal
JBM, e99010042 https://doi.org/10.14440/jbm.2025.0069
Submitted: 20 August 2024 | Revised: 10 November 2024 | Accepted: 13 November 2024 | Published: 26 November 2024
(This article belongs to the Special Issue AI-Driven Empowerment Biosignal’s Applications in Health Systems)
© 2024 by the Journal of Biological Methods published by POL Scientific. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Background: Alzheimer’s disease (AD) is the most common form of dementia. The lack of effective prevention or cure makes AD a significant concern, as it is a progressive disease with symptoms that worsen over time. Objective: The aim of this study is to develop an algorithm capable of differentiating between patients with early-stage AD (mild cognitive impairment [MCI]), moderate AD, and healthy controls (C) using electroencephalogram (EEG) signals. Methods: A publicly available EEG database was utilized, with seven EEG recordings selected from each study group (MCI, AD, and C) to ensure a balanced dataset. For each 1-s segment of EEG data, 43 time-frequency features were computed. These features were then compressed over time using 10 statistical measures. Subsequently, 15 classifiers were employed to distinguish between paired groups using a 7-fold cross-validation. Results: The strategy yielded better results than state-of-the-art methods, achieving a 100% accuracy in both C versus MCI and C versus AD binary classifications. This improvement translated to a 2% increase in accuracy for C versus MCI and a 4% increase for C versus AD, despite a 1.2% decrease in performance for AD versus MCI. In addition, the proposed method outperformed prior work on the same database by 4.8% for the AD versus MCI comparison. Conclusion: The present study highlights the potential of EEG as a promising tool for early AD diagnosis. Nevertheless, a more extensive database should be used to enhance the generalizability of the results in future work.

Keywords
Discrimination
Electroencephalogram
Mild cognitive impairment
Alzheimer’s disease
Funding
This work was supported by national funds from FCT – Fundação para a Ciência e a Tecnologia, under project UIDB/50016/2020.
Conflict of interest
The authors declare no conflicts of interest.
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Journal of Biological Methods, Electronic ISSN: 2326-9901 Print ISSN: TBA, Published by POL Scientific
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