Mutual information entropy research on dementia EEG signals

The aim of this study is to find new components from electroencephalogram (EEG) of Alzheimer´s disease patients. Three parameters based on information theory and nonlinear dynamic, information entropy, mutual entropy and approximate entropy, were computed and the results were analyzed. Compare with normal persons, there is an extensive and significant depression in information activity, transport intensity and complexity of AD patients EEG signals. This result indicates an important possibility to generate a rigorous measure of AD patients from EEG signals in clinical diagnosis.