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

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1935-1941
Number of pages7
ISBN (Print)0780394909, 9780780394902
DOIs
StatePublished - Jan 1 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

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

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period7/16/067/21/06

All Science Journal Classification (ASJC) codes

  • Software

Fingerprint Dive into the research topics of 'Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease'. Together they form a unique fingerprint.

Cite this