Alzheimer's disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer's disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer's disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer's disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer's disease from EEG records.

Department of Computer Science Faculty of Science University of South Bohemia in Ceske Budejovice Ceske Budejovice Czech Republic

Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Technical University Prague Technicka Street 4 16607 Prague 6 Czech Republic

Department of Neurology Faculty of Medicine in University Hospital Hradec Králové Charles University Prague Sokolská 581 500 05 Hradec Králové Czech Republic

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