Abstract
The diagnosis of Alzheimer's disease (AD) at an early stage is a major concern due to growing number of elderly population affected by the disease, as well as the lack of a standard diagnosis procedure available to community clinics. Recent studies have used wavelets and other signal processing methods to analyze EEG signals in an attempt to find a noninvasive biomarker for AD. These studies had varying degrees of success, in part due to small cohort size. In this study, multiresolution wavelet analysis is performed on event related potentials of the EEGs of a relatively larger cohort of 44 patients. Particular emphasis was on diagnosis at the earliest stage and feasibility of implementation in a community health clinic setting. Extracted features were then used to train an ensemble of classifiers based stacked generalization approach. We describe the approach, and present our promising preliminary results.
| Original language | English (US) |
|---|---|
| Title of host publication | 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 |
| Pages | 5350-5353 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Dec 1 2006 |
| Event | 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States Duration: Aug 30 2006 → Sep 3 2006 |
Publication series
| Name | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
|---|---|
| ISSN (Print) | 0589-1019 |
Other
| Other | 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 8/30/06 → 9/3/06 |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
}
Stacked generalization for early diagnosis of Alzheimer's disease. / Gandhi, Hardik; Green, Deborah; Kounios, John; Clark, Christopher M.; Polikar, Robi.
28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. p. 5350-5353 4030428 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Stacked generalization for early diagnosis of Alzheimer's disease
AU - Gandhi, Hardik
AU - Green, Deborah
AU - Kounios, John
AU - Clark, Christopher M.
AU - Polikar, Robi
PY - 2006/12/1
Y1 - 2006/12/1
N2 - The diagnosis of Alzheimer's disease (AD) at an early stage is a major concern due to growing number of elderly population affected by the disease, as well as the lack of a standard diagnosis procedure available to community clinics. Recent studies have used wavelets and other signal processing methods to analyze EEG signals in an attempt to find a noninvasive biomarker for AD. These studies had varying degrees of success, in part due to small cohort size. In this study, multiresolution wavelet analysis is performed on event related potentials of the EEGs of a relatively larger cohort of 44 patients. Particular emphasis was on diagnosis at the earliest stage and feasibility of implementation in a community health clinic setting. Extracted features were then used to train an ensemble of classifiers based stacked generalization approach. We describe the approach, and present our promising preliminary results.
AB - The diagnosis of Alzheimer's disease (AD) at an early stage is a major concern due to growing number of elderly population affected by the disease, as well as the lack of a standard diagnosis procedure available to community clinics. Recent studies have used wavelets and other signal processing methods to analyze EEG signals in an attempt to find a noninvasive biomarker for AD. These studies had varying degrees of success, in part due to small cohort size. In this study, multiresolution wavelet analysis is performed on event related potentials of the EEGs of a relatively larger cohort of 44 patients. Particular emphasis was on diagnosis at the earliest stage and feasibility of implementation in a community health clinic setting. Extracted features were then used to train an ensemble of classifiers based stacked generalization approach. We describe the approach, and present our promising preliminary results.
UR - http://www.scopus.com/inward/record.url?scp=34047107751&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34047107751&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2006.260644
DO - 10.1109/IEMBS.2006.260644
M3 - Conference contribution
C2 - 17947137
AN - SCOPUS:34047107751
SN - 1424400325
SN - 9781424400324
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 5350
EP - 5353
BT - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
ER -