title: Ensemble based data fusion for early diagnosis of Alzheimer's disease.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Rowan University, Glassboro, New Jersey, USA.
authors.forename: Devi
authors.initials: D
authors.lastname: Parikh
authors.forename: Nick
authors.initials: N
authors.lastname: Stepenosky
authors.forename: Apostolos
authors.initials: A
authors.lastname: Topalis
authors.forename: Deborah
authors.initials: D
authors.lastname: Green
authors.forename: John
authors.initials: J
authors.lastname: Kounios
authors.forename: Christopher
authors.initials: C
authors.lastname: Clark
authors.forename: Robi
authors.initials: R
authors.lastname: Polikar