Discrimination of mild cognitive impairment and Alzheimer's disease using transfer entropy measures of scalp EEG

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.

Keywords: EEG-based diagnosis; early Alzheimer's disease; mild cognitive impairment; transfer entropy.

Fig. 1

Regional and Subregional Boundaries for Electrodes. Left: major regions; right: subregions. LF=left frontal; RF=right frontal; F=frontal; LT=left temporal; RT=right temporal; LC=left central; RC=right central; C=central; LP=left parietal; RP=right parietal; P=parietal; O=occipital. Note that central line channels (those with Z in the designation) are excluded from L/R subregions.

Fig. 2

Schematic feature selection process.