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Comput Methods Programs Biomed. 2014 Apr;114(2):153-63. doi: 10.1016/j.cmpb.2014.01.019. Epub 2014 Feb 8.

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

Author information

1
Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN 37996, United States.
2
Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN 37996, United States; National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Knoxville, TN 37996, United States. Electronic address: xzhao9@utk.edu.
3
Oak Ridge National Laboratory, Oak Ridge, TN 37831-6418, United States.
4
Department of Neurology, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States.
5
Department of Behavioral Science, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States.
6
Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States; Department of Statistics, University of Kentucky, Lexington, KY 40356, United States.
7
Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States; Department of Behavioral Science, University of Kentucky, Lexington, KY 40356, United States.

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.

KEYWORDS:

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

PMID:
24598317
PMCID:
PMC4021716
DOI:
10.1016/j.cmpb.2014.01.019
[Indexed for MEDLINE]
Free PMC Article
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