Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis

Alzheimer’s 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...

Descripción completa

Detalles Bibliográficos
Autores: Perez-Valero, Eduardo, Morillas, Christian, Lopez-Gordo, Miguel Angel, Minguillon, Jesus
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/71910
Acceso en línea:http://hdl.handle.net/10230/71910
http://dx.doi.org/10.1142/S0129065723500211
Access Level:acceso abierto
Palabra clave:Alzheimer’s disease
Reduced EEG montage
Wearable EEG
Automated detection
Artificial intelligence
id ES_86e4b7c382ec8ed14ff0d0218fe2a2dd
oai_identifier_str oai:recercat.cat:10230/71910
network_acronym_str ES
network_name_str España
repository_id_str
spelling Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysisPerez-Valero, EduardoMorillas, ChristianLopez-Gordo, Miguel AngelMinguillon, JesusAlzheimer’s diseaseReduced EEG montageWearable EEGAutomated detectionArtificial intelligenceAlzheimer’s 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 (p-value = 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.The authors would like to acknowledge the members and patients of the Cognitive and Behavioral Neurology Unit at Hospital Universitario Virgen de las Nieves de Granada who took part in the study. This research was supported by Projects B-TIC-352-UGR20 and Excellence Research P21 00084 (Junta de Andalucia), PGC2018-098813-B-C31, PGC2018-098813-B-C32, PID2021-128529OA-I00 (MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe) and the Postdoctoral Fellowship Programme of Junta de Andalucia (PAIDI 2020).World Scientific202520252023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/71910http://dx.doi.org/10.1142/S0129065723500211http://hdl.handle.net/10230/71910reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésInternational Journal of Neural Systems. 2023 Apr;33(4):2350021info:eu-repo/grantAgreement/ES/2PE/PGC2018-098813-B-C31info:eu-repo/grantAgreement/ES/2PE/PGC2018-098813-B-C32info:eu-repo/grantAgreement/ES/2PE/PID2021-128529OA-I00© The Author(s). This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND) License which permits use, distribution and reproduction, provided that the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/719102026-05-29T05:05:01Z
dc.title.none.fl_str_mv Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis
title Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis
spellingShingle Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis
Perez-Valero, Eduardo
Alzheimer’s disease
Reduced EEG montage
Wearable EEG
Automated detection
Artificial intelligence
title_short Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis
title_full Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis
title_fullStr Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis
title_full_unstemmed Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis
title_sort Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis
dc.creator.none.fl_str_mv Perez-Valero, Eduardo
Morillas, Christian
Lopez-Gordo, Miguel Angel
Minguillon, Jesus
author Perez-Valero, Eduardo
author_facet Perez-Valero, Eduardo
Morillas, Christian
Lopez-Gordo, Miguel Angel
Minguillon, Jesus
author_role author
author2 Morillas, Christian
Lopez-Gordo, Miguel Angel
Minguillon, Jesus
author2_role author
author
author
dc.subject.none.fl_str_mv Alzheimer’s disease
Reduced EEG montage
Wearable EEG
Automated detection
Artificial intelligence
topic Alzheimer’s disease
Reduced EEG montage
Wearable EEG
Automated detection
Artificial intelligence
description Alzheimer’s 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 (p-value = 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.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/71910
http://dx.doi.org/10.1142/S0129065723500211
http://hdl.handle.net/10230/71910
url http://hdl.handle.net/10230/71910
http://dx.doi.org/10.1142/S0129065723500211
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv International Journal of Neural Systems. 2023 Apr;33(4):2350021
info:eu-repo/grantAgreement/ES/2PE/PGC2018-098813-B-C31
info:eu-repo/grantAgreement/ES/2PE/PGC2018-098813-B-C32
info:eu-repo/grantAgreement/ES/2PE/PID2021-128529OA-I00
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv World Scientific
publisher.none.fl_str_mv World Scientific
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869412407295606784
score 15,81155

Similares en LA Referencia