期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 114, 期 2, 页码 153-163出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2014.01.019
关键词
EEG-based diagnosis; Early Alzheimer's disease; Mild cognitive impairment; Spectral; Entropy
类别
资金
- Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, US Department of Energy [DE-AC05-00OR22725]
- NSF [CMMI-0845753, CMMI-1234155]
- NIH [NIH P30 AG028383, NIH AG00986, NIH NCRR UL1RR033173]
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1300426] Funding Source: National Science Foundation
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. (C) 2014 Elsevier Ireland Ltd. All rights reserved.
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