{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T14:57:58Z","timestamp":1788274678181,"version":"build-2803163510"},"reference-count":0,"publisher":"Journal of Biological Methods","issue":"1","license":[{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JBM"],"abstract":"<jats:p>Background: Alzheimer&amp;rsquo;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.<\/jats:p>","DOI":"10.14440\/jbm.2025.0069","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T08:34:44Z","timestamp":1732696484000},"page":"e99010042","source":"Crossref","is-referenced-by-count":3,"title":["Electroencephalogram-based time-frequency analysis for Alzheimer\u2019s disease detection using machine learning"],"prefix":"10.14440","volume":"12","author":[{"given":"S\u00e9rgio","family":"Daniel Rodrigues","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro","family":"Miguel Rodrigues","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5574","published-online":{"date-parts":[[2024,11,26]]},"container-title":["Journal of Biological Methods"],"original-title":[],"link":[{"URL":"https:\/\/web-api.polscientific.com\/uploads\/file\/asp\/202502281242585bf4a0365.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/web-api.polscientific.com\/uploads\/file\/asp\/202502281242585bf4a0365.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T09:15:15Z","timestamp":1740734115000},"score":1,"resource":{"primary":{"URL":"https:\/\/polscientific.com\/journal\/JBM\/12\/1\/10.14440\/jbm.2025.0069"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,26]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,2,28]]}},"URL":"https:\/\/doi.org\/10.14440\/jbm.2025.0069","relation":{},"ISSN":["2326-9901"],"issn-type":[{"value":"2326-9901","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,26]]}}}