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.
- Dynamic Systems and Control Division
Resting State EEG Multiscale Entropy Dynamics in Mild Cognitive Impairment and Early Alzheimer’s Disease
- Views Icon Views
- Share Icon Share
- Search Site
McBride, J, Zhao, X, Munro, N, Jicha, G, Smith, C, & Jiang, Y. "Resting State EEG Multiscale Entropy Dynamics in Mild Cognitive Impairment and Early Alzheimer’s Disease." Proceedings of the ASME 2014 Dynamic Systems and Control Conference. Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing. San Antonio, Texas, USA. October 22–24, 2014. V002T16A004. ASME. https://doi.org/10.1115/DSCC2014-5912
Download citation file: