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dc.contributor.authorAl-Nuaimi, AHen
dc.contributor.authorJammeh, Een
dc.contributor.authorSun, Len
dc.contributor.authorIfeachor, Een
dc.date.accessioned2019-05-13T05:58:36Z
dc.date.available2019-05-13T05:58:36Z
dc.date.issued2015en
dc.identifier.issn1557-170Xen
dc.identifier.urihttp://hdl.handle.net/10026.1/13830
dc.description.abstract

Alzheimer's disease (AD) and other forms of dementia are one of the major public health and social challenges of our time because of the large number of people affected. Early diagnosis is important for patients and their families to get maximum benefits from access to health and social care services and to plan for the future. EEG provides useful insight into brain functions and can play a useful role as a first line of decision-support tool for early detection and diagnosis of dementia. It is non-invasive, low-cost and has a high temporal resolution. The functions of brain cells are affected by damage caused by dementia and this in turn causes changes in the features of the EEG. Information theoretic methods have emerged as a potentially useful way to quantify changes in the EEG as biomarkers of dementia. Tsallis entropy has been shown to be one of the most promising information theoretic methods for quantifying changes in the EEG. In this paper, we develop the approach further. This has yielded an enhanced performance compared to existing approaches.

en
dc.format.extent4166 - 4169en
dc.language.isoenen
dc.subjectAdulten
dc.subjectAgeden
dc.subjectAged, 80 and overen
dc.subjectAlzheimer Diseaseen
dc.subjectBiomarkersen
dc.subjectCase-Control Studiesen
dc.subjectDatabases, Factualen
dc.subjectDementiaen
dc.subjectEarly Diagnosisen
dc.subjectElectroencephalographyen
dc.subjectEntropyen
dc.subjectHumansen
dc.subjectMiddle Ageden
dc.subjectSignal Processing, Computer-Assisteden
dc.titleTsallis entropy as a biomarker for detection of Alzheimer's disease.en
dc.typeConference Contribution
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/26737212en
plymouth.volume2015en
plymouth.publication-statusPublisheden
plymouth.journalConf Proc IEEE Eng Med Biol Socen
dc.identifier.doi10.1109/EMBC.2015.7319312en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/00 Groups by role/Professional Services staff
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
dc.publisher.placeUnited Statesen
dc.rights.embargoperiodNot knownen
rioxxterms.funderEPSRC
rioxxterms.identifier.projectNovel Point-of-Care Diagnostic Techniques for Dementia
rioxxterms.versionofrecord10.1109/EMBC.2015.7319312en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeConference Paper/Proceeding/Abstracten
plymouth.funderNovel Point-of-Care Diagnostic Techniques for Dementia::EPSRCen
rioxxterms.funder.project961d416c-7c19-4617-8a9c-08d9a525fcde


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