EEG synchrony analysis for early diagnosis of Alzheimer's disease: a study with several synchrony measures and EEG data sets.

It has frequently been reported in the medical literature that the EEG of Alzheimer disease (AD) patients is less synchronous than in healthy subjects. In this paper, it is explored whether loss in EEG synchrony can be used to diagnose AD at an early stage. Multiple synchrony measures are applied to two different EEG data sets: (1) EEG of pre-dementia patients and control subjects; (2) EEG of mild AD patients and control subjects; the two data sets are from different patients, different hospitals, and obtained through different recording systems. It is observed that both Granger causality and stochastic event synchrony indicate statistically significant loss of EEG synchrony, for the two data sets; those two synchrony measures are then combined as features in linear and quadratic discriminant analysis (with crossvalidation), yielding classification rates of 83% and 88% for the pre-dementia data set and mild AD data set respectively. These results suggest that loss in EEG synchrony is indicative for early AD.