Clustering and modeling of EEG coherence features of Alzheimer's and mild cognitive impairment patients

Kwaku Akrofi, Mary C. Baker, Michael W. O'Boyle, Randolph B. Schiffer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

Using multiple discriminant analysis (MDA) and k-means clustering, coherence features extracted from the EEGs of a group of 56 subjects were analyzed to assess how feasible an automated coherence-based pattern recognition system that detects Alzheimer's disease (AD) would be. Sixteen of the subjects were AD patients, 24 were mild cognitive impairment (MCI) patients while 16 were age-matched controls. With MDA, an overall classification rate (CR) of 84% was obtained for AD vs. MCI vs. Controls classifications. The high CR implies that it is possible to distinguish between the three groups. The coherence features were also statistically analyzed to derive a neural model of AD and MCI, which indicated that patients with AD may have a greater number of damaged cortical fibers than their MCI counterparts, and furthermore, that MCI may be an intermediary step in the development of AD.

Original languageEnglish
Title of host publicationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
PublisherIEEE Computer Society
Pages1092-1095
Number of pages4
ISBN (Print)9781424418152
DOIs
StatePublished - 2008
Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
Duration: Aug 20 2008Aug 25 2008

Publication series

NameProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

Conference

Conference30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
Country/TerritoryCanada
CityVancouver, BC
Period08/20/0808/25/08

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