Predictive Models in Diagnosis of Alzheimer’s Disease from EEG

Lucie Tylova, Jaromir Kukal, Oldrich Vysata

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.

Keywords


Alzheimer’s disease; EEG; linear predictive model; quasi-stationarity; robust statistics; multiple testing; FDR.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN 1210-2709 (Print)
ISSN 1805-2363 (Online)
Published by the Czech Technical University in Prague