Diagnostic utility of EEG based biomarkers for Alzheimer's disease

Charlotte Cecere, Christen Corrado, Robi Polikar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479937288
DOIs
StatePublished - Dec 2 2014
Event2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014 - Boston, United States
Duration: Apr 25 2014Apr 27 2014

Publication series

NameProceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC
Volume2014-December

Other

Other2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014
Country/TerritoryUnited States
CityBoston
Period4/25/144/27/14

All Science Journal Classification (ASJC) codes

  • Bioengineering

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