TY - GEN
T1 - Ensemble based data fusion for early diagnosis of Alzheimer's disease
AU - Parikh, Devi
AU - Stepenosky, Nick
AU - Topalis, Apostolos
AU - Green, Deborah
AU - Kounios, John
AU - Clark, Christopher
AU - Polikar, Robi
PY - 2005
Y1 - 2005
N2 - 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 EEC, which are further analyzed using multlresolution 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.
AB - 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 EEC, which are further analyzed using multlresolution 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.
UR - http://www.scopus.com/inward/record.url?scp=33846935037&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33846935037&partnerID=8YFLogxK
U2 - 10.1109/iembs.2005.1616971
DO - 10.1109/iembs.2005.1616971
M3 - Conference contribution
AN - SCOPUS:33846935037
SN - 0780387406
SN - 9780780387409
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 2479
EP - 2482
BT - Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Y2 - 1 September 2005 through 4 September 2005
ER -