Journal article
An ensemble based data fusion approach for early diagnosis of Alzheimer’s disease
Information fusion, v 9(1), pp 83-95
2008
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
As the number of the elderly population affected by Alzheimer’s disease (AD) rises rapidly, the need to find an accurate, inexpensive and non-intrusive diagnostic procedure that can be made available to community healthcare providers is becoming an increasingly urgent public health concern. Several recent studies have looked at analyzing electroencephalogram (EEG) signals through the use of wavelets and neural networks. While showing great promise, the final outcomes of these studies have been largely inconclusive. This is mostly due to inherent difficulty of the problem, but also – perhaps – due to inefficient use of the available information, as many of these studies have used a single EEG channel for the analysis. In this contribution, we describe an ensemble of classifiers based data fusion approach to combine information from two or more sources, believed to contain complementary information, for early diagnosis of Alzheimer’s disease. Our emphasis is on sequentially generating an ensemble of classifiers that explicitly seek the most discriminating information from each data source. Specifically, we use the event related potentials recorded from the Pz, Cz, and Fz electrodes of the EEG, decomposed into different frequency bands using multiresolution wavelet analysis. The proposed data fusion approach includes generating multiple classifiers trained with strategically selected subsets of the training data from each source, which are then combined through a modified weighted majority voting procedure. The implementation details and the promising outcomes of this implementation are presented.
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Details
- Title
- An ensemble based data fusion approach for early diagnosis of Alzheimer’s disease
- Creators
- Robi Polikar - Rowan UniversityApostolos Topalis - Rowan UniversityDevi Parikh - Rowan UniversityDeborah Green - Drexel UniversityJennifer Frymiare - Drexel UniversityJohn Kounios - Drexel UniversityChristopher M. Clark - University of Pennsylvania
- Publication Details
- Information fusion, v 9(1), pp 83-95
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000251070200008
- Scopus ID
- 2-s2.0-35348887748
- Other Identifier
- 991019168393404721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods