Publication
Electroencephalogram-based time-frequency analysis for Alzheimer’s disease detection using machine learning
| dc.contributor.author | Rodrigues, Sérgio Daniel | |
| dc.contributor.author | Rodrigues, Pedro Miguel | |
| dc.date.accessioned | 2025-01-27T18:39:02Z | |
| dc.date.available | 2025-01-27T18:39:02Z | |
| dc.date.issued | 2024-11-26 | |
| dc.description.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. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.14440/jbm.2025.0069 | pt_PT |
| dc.identifier.issn | 2326-9901 | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/47926 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Discrimination | pt_PT |
| dc.subject | Electroencephalogram | pt_PT |
| dc.subject | Mild cognitive impairment | pt_PT |
| dc.subject | Alzheimer’s disease | pt_PT |
| dc.title | Electroencephalogram-based time-frequency analysis for Alzheimer’s disease detection using machine learning | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Journal of Biological Methods | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |