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

Authors:
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
Affiliations:
-;-;-;-;-;-;-;-;-;-
Venue:
Computer Methods and Programs in Biomedicine
Year:
2014

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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.