NDLI: EEG and MRI data fusion for early diagnosis of Alzheimer's disease

Content Provider	IEEE Xplore Digital Library
Author	Patel, Tejash Polikar, Robi Davatzikos, Christos Clark, Christopher M.
Copyright Year	2008
Description	Author affiliation: Dept. of Radiology, University of Pennsylvania, Philadelphia, USA (Davatzikos, Christos) \|\| Signal Processing and Pattern Recognition Laboratory of the Electrical and Computer Eng. Dept. at Rowan University, Glassboro, NJ 08028, USA (Patel, Tejash; Polikar, Robi) \|\| Department of Neurology, University of Pennsylvania, Philadelphia, USA (Clark, Christopher M.)
Abstract	The prevalence of Alzheimer's disease (AD) is rising alarmingly as the average age of our population increases. There is no treatment to halt or slow the pathology responsible for AD, however, new drugs are promising to reduce the rate of progression. On the other hand, the efficacy of these new medications critically depends on our ability to diagnose AD at the earliest stage. Currently AD is diagnosed through longitudinal clinical evaluations, which are available only at specialized dementia clinics, hence beyond financial and geographic reach of most patients. Automated diagnosis tools that can be made available to community hospitals would therefore be very beneficial. To that end, we have previously shown that the event related potentials obtained from different scalp locations can be effectively used for early diagnosis of AD using an ensemble of classifiers based decision fusion approach. In this study, we expand our data fusion approach to include MRI based measures of regional brain atrophy. Our initial results indicate that ERPs and MRI carry complementary information, and the combination of these heterogeneous data sources using a decision fusion approach can significantly improve diagnostic accuracy.
Starting Page	1757
Ending Page	1760
File Size	456379
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4649517
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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