Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer's disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.

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Published in Journal of Healthcare Engineering, v. 6, no. 1, p. 55-70.

Copyright © 2015 Hindawi Publishing Corporation.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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This research was sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under Contract No. DE-AC05-00OR22725; by the NSF under grant numbers CMMI-0845753 and CMMI-1234155; and in part by the NIH under grants NIH P30 AG028383 to UK Sanders-Brown Center on Aging, NIH AG00986 to YJ, and NIH NCRR UL1RR033173 to UK Center for Clinical and Translational Science.