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

Sérgio Daniel Rodrigues, Pedro Miguel Rodrigues*

*Autor correspondente para este trabalho

Resultado de pesquisarevisão de pares

Resumo

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.
Idioma originalEnglish
Número do artigoe99010042
Número de páginas12
RevistaJournal of Biological Methods
DOIs
Estado da publicaçãoAceite para publicação - 26 nov. 2024

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