An SFFS technique for EEG feature classification to identify sub-groups

Mary C. Baker, Andy S. Kerr, Elizabeth Hames, Kwaku Akrofi

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

5 Scopus citations

Abstract

Pattern recognition techniques can be applied to problems in medicine to aid diagnostic accuracy and uncover patterns associated with disease states that are not always obvious to the clinician. In this work, a sequential forward floating search technique (SFFS) was applied to the problem of classification of patients with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls. The technique resulted in superior classification rates over statistical methods, as described in the paper. The advantage of SFFS may lie in the technique's ability to identify sub-groups within diagnostic categories, and to correctly select features that identify those sub-groups.

Original languageEnglish
Title of host publicationProceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012
DOIs
StatePublished - 2012
Event25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 - Rome, Italy
Duration: Jun 20 2012Jun 22 2012

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
ISSN (Print)1063-7125

Conference

Conference25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012
CountryItaly
CityRome
Period06/20/1206/22/12

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