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

@inproceedings{citeulike:4509076, 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.}, author = {Patel, Tejash and Polikar, Robi and Davatzikos, Christos and Clark, Christopher M.}, booktitle = {Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE}, citeulike-article-id = {4509076}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/IEMBS.2008.4649517}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4649517}, doi = {10.1109/IEMBS.2008.4649517}, journal = {Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE}, keywords = {alzheimer, erp}, pages = {1757--1760}, posted-at = {2009-05-13 02:44:31}, priority = {3}, title = {EEG and MRI data fusion for early diagnosis of Alzheimer's disease}, url = {http://dx.doi.org/10.1109/IEMBS.2008.4649517}, year = {2008} }

TY - CONF ID - citeulike:4509076 L3 - citeulike-article-id:4509076 N2 - 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. JF - Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE EP - 1760 TI - EEG and MRI data fusion for early diagnosis of Alzheimer's disease T2 - Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE SP - 1757 KW - alzheimer KW - erp AU - Patel, Tejash AU - Polikar, Robi AU - Davatzikos, Christos AU - Clark, Christopher PY - 2008/// UR - http://dx.doi.org/10.1109/IEMBS.2008.4649517 ER -