Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease

Nicholas Stepenosky; Deborah Green; John Kounios; Christopher M. Clark; Robi Polikar

Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease

Nicholas Stepenosky, Deborah Green, John Kounios, Christopher M. Clark, Robi Polikar

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

Abstract

With the rapid increase in the population of elderly individuals affected by Alzheimer's disease, the need for an accurate, inexpensive and non-intrusive diagnostic biomarker that can be made available to community healthcare providers presents itself as a major public health concern. The feasibility of EEG as such a biomarker has gained a renewed attention as several recent studies, including our previous efforts, reported promising results. In this paper we present our preliminary results on using wavelet coefficients of event related potentials along with an ensemble of classifiers combined with majority vote and decision templates.

Original language	English (US)
Title of host publication	2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
Pages	V901-V904
State	Published - 2006
Externally published	Yes
Event	2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France Duration: May 14 2006 → May 19 2006

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	5
ISSN (Print)	1520-6149

Other

Other	2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Country/Territory	France
City	Toulouse
Period	5/14/06 → 5/19/06

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Cite this

Stepenosky, N., Green, D., Kounios, J., Clark, C. M., & Polikar, R. (2006). Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease. In 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings (pp. V901-V904). Article 1661422 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 5).

Stepenosky, Nicholas ; Green, Deborah ; Kounios, John et al. / Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease. 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings. 2006. pp. V901-V904 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{d5dd3007478446d886b5e9cf043610e9,

title = "Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease",

abstract = "With the rapid increase in the population of elderly individuals affected by Alzheimer's disease, the need for an accurate, inexpensive and non-intrusive diagnostic biomarker that can be made available to community healthcare providers presents itself as a major public health concern. The feasibility of EEG as such a biomarker has gained a renewed attention as several recent studies, including our previous efforts, reported promising results. In this paper we present our preliminary results on using wavelet coefficients of event related potentials along with an ensemble of classifiers combined with majority vote and decision templates.",

author = "Nicholas Stepenosky and Deborah Green and John Kounios and Clark, {Christopher M.} and Robi Polikar",

year = "2006",

language = "English (US)",

isbn = "142440469X",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "V901--V904",

booktitle = "2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings",

note = "2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 ; Conference date: 14-05-2006 Through 19-05-2006",

}

Stepenosky, N, Green, D, Kounios, J, Clark, CM & Polikar, R 2006, Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease. in 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings., 1661422, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 5, pp. V901-V904, 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, Toulouse, France, 5/14/06.

Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease. / Stepenosky, Nicholas; Green, Deborah; Kounios, John et al.
2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings. 2006. p. V901-V904 1661422 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 5).

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

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T1 - Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease

AU - Stepenosky, Nicholas

AU - Green, Deborah

AU - Kounios, John

AU - Clark, Christopher M.

AU - Polikar, Robi

PY - 2006

Y1 - 2006

N2 - With the rapid increase in the population of elderly individuals affected by Alzheimer's disease, the need for an accurate, inexpensive and non-intrusive diagnostic biomarker that can be made available to community healthcare providers presents itself as a major public health concern. The feasibility of EEG as such a biomarker has gained a renewed attention as several recent studies, including our previous efforts, reported promising results. In this paper we present our preliminary results on using wavelet coefficients of event related potentials along with an ensemble of classifiers combined with majority vote and decision templates.

AB - With the rapid increase in the population of elderly individuals affected by Alzheimer's disease, the need for an accurate, inexpensive and non-intrusive diagnostic biomarker that can be made available to community healthcare providers presents itself as a major public health concern. The feasibility of EEG as such a biomarker has gained a renewed attention as several recent studies, including our previous efforts, reported promising results. In this paper we present our preliminary results on using wavelet coefficients of event related potentials along with an ensemble of classifiers combined with majority vote and decision templates.

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AN - SCOPUS:33947696720

SN - 142440469X

SN - 9781424404698

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - V901-V904

BT - 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings

T2 - 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006

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ER -

Stepenosky N, Green D, Kounios J, Clark CM, Polikar R. Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease. In 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings. 2006. p. V901-V904. 1661422. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Majority vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer's disease

Abstract

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All Science Journal Classification (ASJC) codes

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