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dc.contributor.authorAl-Nuaimi, AHen
dc.contributor.authorJammeh, Een
dc.contributor.authorSun, Len
dc.contributor.authorIfeachor, Een
dc.date.accessioned2019-05-13T05:55:04Z
dc.date.available2019-05-13T05:55:04Z
dc.date.issued2017-07en
dc.identifier.issn1557-170Xen
dc.identifier.urihttp://hdl.handle.net/10026.1/13828
dc.description.abstract

It is widely accepted that early diagnosis of Alzheimer's disease (AD) makes it possible for patients to gain access to appropriate health care services and would facilitate the development of new therapies. AD starts many years before its clinical manifestations and a biomarker that provides a measure of changes in the brain in this period would be useful for early diagnosis of AD. Given the rapid increase in the number of older people suffering from AD, there is a need for an accurate, low-cost and easy to use biomarkers that could be used to detect AD in its early stages. Potentially, the electroencephalogram (EEG) can play a vital role in this but at present, no reliable EEG biomarker exists for early diagnosis of AD. The gradual slowing of brain activity caused by AD starts from the back of the brain and spreads out towards other parts. Consequently, determining the brain regions that are first affected by AD may be useful in its early diagnosis. Higuchi fractal dimension (HFD) has characteristics which make it suited to capturing region-specific neural changes due to AD. The aim of this study is to investigate the potential of HFD of the EEG as a biomarker which is associated with the brain region first affected by AD. Mean HFD value was calculated for all channels of EEG signals recorded from 52 subjects (20-AD and 32-normal). Then, p-values were calculated between the two groups (AD and normal) to detect EEG channels that have a significant association with AD. k-nearest neighbor (KNN) algorithm was used to compute the distance between AD patients and normal subjects in the classification. Our results show that AD patients have significantly lower HFD values in the parietal areas. HFD values for channels in these areas were used to discriminate between AD and normal subjects with a sensitivity and specificity values of 100% and 80%, respectively.

en
dc.format.extent2320 - 2324en
dc.language.isoenen
dc.subjectAlzheimer Diseaseen
dc.subjectBiomarkersen
dc.subjectEarly Diagnosisen
dc.subjectElectroencephalographyen
dc.subjectFractalsen
dc.subjectHumansen
dc.titleHiguchi fractal dimension of the electroencephalogram as a biomarker for early detection of Alzheimer's disease.en
dc.typeConference Contribution
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/29060362en
plymouth.volume2017en
plymouth.publication-statusPublisheden
plymouth.journalConf Proc IEEE Eng Med Biol Socen
dc.identifier.doi10.1109/EMBC.2017.8037320en
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.2017.8037320en
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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