Abstract
Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia such as Alzheimer’s disease (AD). This study explores non-event-related multiscale entropy (MSE) measures as features 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 (NC), 16 MCI, and 17 early AD — are examined. Multiscale entropy curves are computed for short EEG segments and averaged over the segments. Binary discriminations among the three groups are conducted using support vector machine models. Leave-one-out cross-validation accuracies of 80.7% (p-value <0.0018) for MCI vs. NC, 87.5% (p-value <1.322E−4) for AD vs. NC, and 90.9% (p-value <2.788E−5) for MCI vs. AD are achieved. Results demonstrate influence of cognitive deficits on multiscale entropy dynamics of non-event-related EEG.
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