Conference proceeding
Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease
2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, pp 1935-1941
01 Jan 2006
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Ensemble techniques with weighted combination rules for early diagnosis of Alzheimer's disease
- Creators
- Nicholas Stepenosky - Rowan UniversityJohn Kounios - Rowan UniversityChristopher M. Clark - Univ Penn, Dept Neurol, Philadelphia, PA USARobi Polikar - Rowan Univ, Dept Elect & Comp Engn, Glassboro, NJ 08028 USAIEEE
- Publication Details
- 2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, pp 1935-1941
- Series
- IEEE International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Number of pages
- 2
- Grant note
- P30 AG10124; R01 AG022272 / National Institute on Aging of the National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Aging (NIA) ECS-0239090 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000245125903049
- Scopus ID
- 2-s2.0-40649101022
- Other Identifier
- 991019170378004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence