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

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

doi:10.1109/CBMS.2012.6266361

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

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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 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 language	English
Title of host publication	Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012
DOIs	https://doi.org/10.1109/CBMS.2012.6266361
State	Published - 2012
Event	25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 - Rome, Italy Duration: Jun 20 2012 → Jun 22 2012

Publication series

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

Conference

Conference	25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012
Country/Territory	Italy
City	Rome
Period	06/20/12 → 06/22/12

Access to Document

10.1109/CBMS.2012.6266361

Cite this

@inproceedings{da1ed5b9666b41fbac75888d8d3a8b0d,

title = "An SFFS technique for EEG feature classification to identify sub-groups",

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.",

author = "Baker, {Mary C.} and Kerr, {Andy S.} and Elizabeth Hames and Kwaku Akrofi",

year = "2012",

doi = "10.1109/CBMS.2012.6266361",

language = "English",

isbn = "9781467320511",

series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

booktitle = "Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012",

note = "25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 ; Conference date: 20-06-2012 Through 22-06-2012",

}

Baker, MC, Kerr, AS, Hames, E & Akrofi, K 2012, An SFFS technique for EEG feature classification to identify sub-groups. in Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012., 6266361, Proceedings - IEEE Symposium on Computer-Based Medical Systems, 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012, Rome, Italy, 06/20/12. https://doi.org/10.1109/CBMS.2012.6266361

An SFFS technique for EEG feature classification to identify sub-groups. / Baker, Mary C.; Kerr, Andy S.; Hames, Elizabeth et al.
Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012. 2012. 6266361 (Proceedings - IEEE Symposium on Computer-Based Medical Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kerr, Andy S.

AU - Hames, Elizabeth

AU - Akrofi, Kwaku

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

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U2 - 10.1109/CBMS.2012.6266361

DO - 10.1109/CBMS.2012.6266361

M3 - Conference contribution

AN - SCOPUS:84867317701

SN - 9781467320511

T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems

BT - Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012

T2 - 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012

Y2 - 20 June 2012 through 22 June 2012

ER -

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

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