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dc.contributor.authorAl-Nuaimi, AHen
dc.contributor.authorJammeh, Een
dc.contributor.authorSun, Len
dc.contributor.authorIfeachor, Een
dc.contributor.authorAl-Nuaimi, AHen
dc.contributor.authorJammeh, Een
dc.contributor.authorLingfen Sunen
dc.contributor.authorIfeachor, Een
dc.contributor.authorJammeh, Een
dc.contributor.authorSun, Len
dc.contributor.authorAl-Nuaimi, AHen
dc.contributor.authorIfeachor, Een
dc.date.accessioned2019-01-30T14:57:31Z
dc.date.available2019-01-30T14:57:31Z
dc.date.issued2016-08en
dc.identifier.issn1557-170Xen
dc.identifier.urihttp://hdl.handle.net/10026.1/13251
dc.description.abstract

The rapid increase in the number of older people with Alzheimer's disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage in the brain due to AD leads to changes in the information processing activity of the brain and the EEG which can be quantified as a biomarker. The objective of the study reported in this paper is to develop robust EEG-based biomarkers for detecting AD in its early stages. We present a new approach to quantify the slowing of the EEG, one of the most consistent features at different stages of dementia, based on changes in the EEG amplitudes (ΔEEGA). The new approach has sensitivity and specificity values of 100% and 88.88%, respectively, and outperformed the Lempel-Ziv Complexity (LZC) approach in discriminating between AD and normal subjects.

en
dc.format.extent993 - 996en
dc.language.isoenen
dc.titleChanges in the EEG amplitude as a biomarker for early detection of Alzheimer's disease.en
dc.typeConference Contribution
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/28226665en
plymouth.volume2016en
plymouth.publication-statusPublisheden
plymouth.journalConf Proc IEEE Eng Med Biol Socen
dc.identifier.doi10.1109/EMBC.2016.7590869en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeUnited Statesen
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1109/EMBC.2016.7590869en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeConference Paper/Proceeding/Abstracten


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