Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease

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

doi:10.1109/ijcnn.2006.246937

Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease

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

Electrical Engineering

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

4 Scopus citations

Abstract

As the population of our elderly suffering from Alzheimer's disease increases rapidly, the need for an accurate, inexpensive and non-intrusive diagnostic procedure that can be made available to local community clinics becomes an increasingly critical public health concern. We propose multiresolution analysis of the electroencephalogram (EEG) followed by an ensemble based classification designed to fuse data from different EEG channels. Several classifier combination rules, including competence based weighted combination have been implemented to evaluate their data fusion performance, with particular emphasis on diagnosing the disease at its earliest stages. Diagnostic performance of the proposed approach has been very promising.

Original language	English (US)
Title of host publication	International Joint Conference on Neural Networks 2006, IJCNN '06
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1935-1941
Number of pages	7
ISBN (Print)	0780394909, 9780780394902
DOIs	https://doi.org/10.1109/ijcnn.2006.246937
State	Published - Jan 1 2006
Event	International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada Duration: Jul 16 2006 → Jul 21 2006

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)	1098-7576

Other

Other	International Joint Conference on Neural Networks 2006, IJCNN '06
Country	Canada
City	Vancouver, BC
Period	7/16/06 → 7/21/06

All Science Journal Classification (ASJC) codes

Software

Access to Document

10.1109/ijcnn.2006.246937

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Stepenosky, N., Kounios, J., Clark, C. M., & Polikar, R. (2006). Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease. In International Joint Conference on Neural Networks 2006, IJCNN '06 (pp. 1935-1941). [1716347] (IEEE International Conference on Neural Networks - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ijcnn.2006.246937

Stepenosky, Nicholas ; Kounios, John ; Clark, Christopher M. ; Polikar, Robi. / Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease. International Joint Conference on Neural Networks 2006, IJCNN '06. Institute of Electrical and Electronics Engineers Inc., 2006. pp. 1935-1941 (IEEE International Conference on Neural Networks - Conference Proceedings).

@inproceedings{48f68dd473954fb8a867967c17b1284a,

title = "Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease",

abstract = "As the population of our elderly suffering from Alzheimer's disease increases rapidly, the need for an accurate, inexpensive and non-intrusive diagnostic procedure that can be made available to local community clinics becomes an increasingly critical public health concern. We propose multiresolution analysis of the electroencephalogram (EEG) followed by an ensemble based classification designed to fuse data from different EEG channels. Several classifier combination rules, including competence based weighted combination have been implemented to evaluate their data fusion performance, with particular emphasis on diagnosing the disease at its earliest stages. Diagnostic performance of the proposed approach has been very promising.",

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

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note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",

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Stepenosky, N, Kounios, J, Clark, CM & Polikar, R 2006, Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease. in International Joint Conference on Neural Networks 2006, IJCNN '06., 1716347, IEEE International Conference on Neural Networks - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1935-1941, International Joint Conference on Neural Networks 2006, IJCNN '06, Vancouver, BC, Canada, 7/16/06. https://doi.org/10.1109/ijcnn.2006.246937

Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease. / Stepenosky, Nicholas; Kounios, John; Clark, Christopher M.; Polikar, Robi.

International Joint Conference on Neural Networks 2006, IJCNN '06. Institute of Electrical and Electronics Engineers Inc., 2006. p. 1935-1941 1716347 (IEEE International Conference on Neural Networks - Conference Proceedings).

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

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AB - As the population of our elderly suffering from Alzheimer's disease increases rapidly, the need for an accurate, inexpensive and non-intrusive diagnostic procedure that can be made available to local community clinics becomes an increasingly critical public health concern. We propose multiresolution analysis of the electroencephalogram (EEG) followed by an ensemble based classification designed to fuse data from different EEG channels. Several classifier combination rules, including competence based weighted combination have been implemented to evaluate their data fusion performance, with particular emphasis on diagnosing the disease at its earliest stages. Diagnostic performance of the proposed approach has been very promising.

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Stepenosky N, Kounios J, Clark CM, Polikar R. Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease. In International Joint Conference on Neural Networks 2006, IJCNN '06. Institute of Electrical and Electronics Engineers Inc. 2006. p. 1935-1941. 1716347. (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/ijcnn.2006.246937