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J. Neur. Syst."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is the most prevalent form of dementia. Although there is no current cure, medical treatment can help to control its progression. Hence, early-stage diagnosis is crucial to maximize the living standards of the patients. Biochemical markers and medical imaging in combination with neuropsychological tests represent the most extended diagnosis procedure. However, these techniques require specialized personnel and long processing time. Furthermore, the access to some of these techniques is often limited in crowded healthcare systems and rural areas. In this context, electroencephalography (EEG), a non-invasive technique to obtain endogenous brain information, has been proposed for the diagnosis of early-stage AD. Despite the valuable information provided by clinical EEG and high density montages, these approaches are impractical in conditions such as those described above. Consequently, in this study, we evaluated the feasibly of using a reduced EEG montage with only four channels to detect early-stage AD. For this purpose, we involved eight clinically diagnosed AD patients and eight healthy controls. The results we obtained reveal similar accuracies ([Formula: see text]-value[Formula: see text]0.66) for the reduced montage (0.86) and a 16-channel montage (0.87). This suggests that a four-channel wearable EEG system could be an effective tool for supporting early-stage AD detection.<\/jats:p>","DOI":"10.1142\/s0129065723500211","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T05:29:05Z","timestamp":1674192545000},"source":"Crossref","is-referenced-by-count":21,"title":["Supporting the Detection of Early Alzheimer\u2019s Disease with a Four-Channel EEG Analysis"],"prefix":"10.1142","volume":"33","author":[{"given":"Eduardo","family":"Perez-Valero","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Automation and Robotics and Research Centre for Information and Communications Technologies, University of Granada, Periodista Daniel Saucedo Aranda, Granada 18071, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Morillas","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Automation and Robotics and Research Centre for Information and Communications Technologies, University of Granada, Periodista Daniel Saucedo Aranda, Granada 18071, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel A.","family":"Lopez-Gordo","sequence":"additional","affiliation":[{"name":"Department of Signal Theory, Telematics and Communications and Research Centre for Information and Communications Technologies, University of Granada, Periodista Daniel Saucedo Aranda, Granada 18071, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesus","family":"Minguillon","sequence":"additional","affiliation":[{"name":"Department of Signal Theory, Telematics and Communications and Research Centre for Information and Communications Technologies, University of Granada, Periodista Daniel Saucedo Aranda, Granada 18071, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2023,2,16]]},"reference":[{"key":"S0129065723500211BIB002","first-page":"436","volume-title":"Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications","author":"Perez-Valero E.","year":"2022"},{"key":"S0129065723500211BIB003","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1212\/WNL.0000000000001991","volume":"85","author":"Palmqvist S.","year":"2015","journal-title":"Neurology"},{"key":"S0129065723500211BIB004","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1001\/jamaneurol.2013.5847","volume":"71","author":"Bloom G. 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