TY - GEN
T1 - Diagnostic utility of EEG based biomarkers for Alzheimer's disease
AU - Cecere, Charlotte
AU - Corrado, Christen
AU - Polikar, Robi
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/2
Y1 - 2014/12/2
N2 - Alzheimer's disease (AD) is a neurodegenerative disease whose definitive diagnosis is only possible via autopsy. Currently used diagnostic approaches include the traditional neuropsychological tests, and recently more objective biomarkers, such as those obtained from cerebral spinal fluid (CSF), magnetic imaging resonance (MRI), and positron emission tomography (PET). Electroencephalography (EEG), a lower cost and non-invasive alternative, has been previously tried but with mixed success. In this effort, we attempt a more comprehensive analysis and comparison of machine learning approaches using EEG based features to determine diagnostic utility of the EEG. We compared support vector machine (SVM), naïve Bayes, multilayer perceptron (MLP), CART trees, k-nearest neighbor (kNN), and AdaBoost on various sets of features extracted from event related potentials (ERP) of the EEG. Our analysis suggests that there is indeed diagnostically useful information in the ERP of the EEG for early diagnosis of AD.
AB - Alzheimer's disease (AD) is a neurodegenerative disease whose definitive diagnosis is only possible via autopsy. Currently used diagnostic approaches include the traditional neuropsychological tests, and recently more objective biomarkers, such as those obtained from cerebral spinal fluid (CSF), magnetic imaging resonance (MRI), and positron emission tomography (PET). Electroencephalography (EEG), a lower cost and non-invasive alternative, has been previously tried but with mixed success. In this effort, we attempt a more comprehensive analysis and comparison of machine learning approaches using EEG based features to determine diagnostic utility of the EEG. We compared support vector machine (SVM), naïve Bayes, multilayer perceptron (MLP), CART trees, k-nearest neighbor (kNN), and AdaBoost on various sets of features extracted from event related potentials (ERP) of the EEG. Our analysis suggests that there is indeed diagnostically useful information in the ERP of the EEG for early diagnosis of AD.
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U2 - 10.1109/NEBEC.2014.6972751
DO - 10.1109/NEBEC.2014.6972751
M3 - Conference contribution
AN - SCOPUS:84940702403
T3 - Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC
BT - Proceedings - 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014
Y2 - 25 April 2014 through 27 April 2014
ER -