Predictive Models in Diagnosis of Alzheimer’s Disease from EEG

Abstract

The fluctuation of an EEG signal is a useful symptom of EEG quasi-stationarity. Linear predictive models of three types and their prediction error are studied via traditional and robust measures. The resulting EEG characteristics are applied to the diagnosis of Alzehimer’s disease. Our aim is to decide among: forward, backward, and predictive models, EEG channels, and also robust and non-robust variability measures, and then to find statistically significant measures for use in the diagnosis of Alzheimer’s disease from EEG.