Abstract
Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer's disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG 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, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.
Highlights
Mild cognitive impairment (MCI) is a memory and cognition disruption associated with old age and a departure from normal aging
Binary Discrimination Results Two-sample Student’s t-distribution tests are performed on group means of peak inter-regional transfer entropy delays (PITEDs) selected as features for binary classifiers in order to determine if observed differences are significant enough to infer a linear separability at the population level
MCI vs. NC Results for leave-one-out cross-validation (LOOCV) accuracies for each binary classifier are presented in Table 2 along with the selected features
Summary
Mild cognitive impairment (MCI) is a memory and cognition disruption associated with old age and a departure from normal aging. While standard time-delayed mutual information fails to distinguish information that is exchanged from shared information due to common history and input signals, transfer entropy is able to effectively distinguish driving and responding elements and to detect asymmetry in the interaction of subsystems [3]. The fact that it is non-symmetric enables one to infer the direction of information flow. This work aims to develop easy neural indicators to diagnose and predict cognitive decline pre-clinically, such as among people with subjective memory complaints
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