Adaptive classification of EEG for dementia diagnosis

The paper presents new approach to dementia detection in time series of measured EEG. The proposed method introduced in this paper evaluates EEG signal according to included novelty. This novelty is evaluated using prediction error and increment of adaptive weights obtained during adaptive prediction of individual EEG channels. Normalization of learning rate was used for adaptation of predictor. The linear dynamic neuron was used as a predictor with gradient descent adaptation. The method was cross-validated on dataset containing 110 patients suffering with dementia and 110 controls. Best achieved results with our method tested on validation dataset were 90% of specificity and 90% of sensitivity. The achievements and limiting assumptions of this method are discussed as well.