Journal article
Ensemble based data fusion for early diagnosis of Alzheimer's disease
Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2005, pp 2479-2482
2005
PMID: 17282740
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We describe an ensemble of classifiers based data fusion approach to combine information from two sources, believed to contain complimentary information, for early diagnosis of Alzheimer's disease. Specifically, we use the event related potentials recorded from the Pz and Cz electrodes of the EEG, which are further analyzed 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 weighted majority voting. Several factors set this study apart from similar prior efforts: we use a larger cohort, specifically target early diagnosis of the disease, use an ensemble based approach rather then a single classifier, and most importantly, we combine information from multiple sources, rather then using a single modality. We present promising results obtained from the first 35 (of 80) patients whose data are analyzed thus far.
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Details
- Title
- Ensemble based data fusion for early diagnosis of Alzheimer's disease
- Creators
- Devi Parikh - Department of Electrical and Computer Engineering, Rowan University, Glassboro, New Jersey, USANick StepenoskyApostolos TopalisDeborah GreenJohn KouniosChristopher ClarkRobi Polikar
- Publication Details
- Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2005, pp 2479-2482
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE); United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000238998402073
- Scopus ID
- 2-s2.0-33846935037
- Other Identifier
- 991014878024904721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Biomedical