Journal article
Boosting based classification of event related potentials for early diagnosis of Alzheimer's disease
Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2005, pp 2494-2497
2005
PMID: 17282744
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
With the number of the elderly population affected by Alzheimer's disease (AD) rising, the need to find an accurate, inexpensive and non-intrusive procedure that can be made available to community healthcare providers for early diagnosis of Alzheimer's disease is becoming more and more urgent as a major health concern. Several recent studies have looked at analyzing electroencephalogram signals through the use of wavelets and neural networks. In this study, multiresolution wavelet analysis, coupled with the ensemble of classifiers based boosting algorithm is used on the P300 component of the event related potentials (ERP) to determine the feasibility of the approach as a diagnostic tool for early diagnosis of AD. The technique and its promising initial results are presented.
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Details
- Title
- Boosting based classification of event related potentials for early diagnosis of Alzheimer's disease
- Creators
- Nicholas Stepenosky - Department of Electrical and Computer Engineering, Rowan University, Glassboro, New Jersey, USAApostolos TopalisHussein SyedDeborah GreenJohn KouniosChristopher ClarkRobi Polikar
- Publication Details
- Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2005, pp 2494-2497
- 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:000238998402077
- Scopus ID
- 2-s2.0-33846923411
- Other Identifier
- 991014877905604721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
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- Web of Science research areas
- Engineering, Biomedical