Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Joseph C. McBride, Xiaopeng Zhao, Nancy B. Munro, Charles D. Smith, Gregory A. Jicha, Lee Hively, Lucas S. Broster, Frederick A. Schmitt, Richard J. Kryscio, Yang Jiang

Research output: Contribution to journalArticlepeer-review

137 Scopus citations

Abstract

Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.

Original languageEnglish
Pages (from-to)153-163
Number of pages11
JournalComputer Methods and Programs in Biomedicine
Volume114
Issue number2
DOIs
StatePublished - Apr 2014

Bibliographical note

Funding Information:
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. The contributions to this paper by two of the authors (N. B. Munro and L. M. Hively) was prepared while acting in their own independent capacities and not on behalf of UT-Battelle, LLC, or its affiliates or successors, or Oak Ridge National Laboratory, or the US Department of Energy.

Keywords

  • EEG-based diagnosis
  • Early Alzheimer's disease
  • Entropy
  • Mild cognitive impairment
  • Spectral

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

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