Discrimination of Mild Alzheimer's Disease Patients using Cluster Analysis of Information Transmission in EEG

Abstract

A description method of information transmission was presented and applied to EEGs recorded from groups of mild Alzheimer's disease (AD) patients and normal controls. To monitor the general interdependence of EEG channels, time-delayed mutual information was computed using the Fraser- Swinney algorithm. To manage the stochastic nature of mutual information and global connectivity of the brain, the modified Karhunen-Loeve decomposition (information transmission map: ITM), which was conducted as a quantification method of the resulting stochastic mutual information, was used. The integration of mutual information with Omega complexity elevated the discrimination power for mild AD. This provides a good procedure, capable of capturing the fine structure in the global information transmission between brain parts, compared with mutual information calculation alone. Compared with normal aged controls, mild AD patients showed significantly different short-time clustered behavior in the information transmission from the EEG. They also show a peculiar pattern of information flows from the successive snapshot monitoring. The observations suggest a discrimination of the mild AD patients from the normal aged controls.