Automatic recognition of Alzheimer's disease with single channel EEG recording

We propose an automatic recognition method of Alzheimer's disease (AD) with single channel EEG recording using combined the genetic algorithms (GA) and the artificial neural network (ANN). The ERP in an auditory oddball task and five min of the resting spontaneous EEG were recorded at P4 site in 16 early AD patients and 16 age-matched controls. EEG and ERP were analyzed to compute their 28 statistical and 2 nonlinear features as well as 88 spectral features, to make a feature pool. The combined GA/ANN was applied to find the minimal set of the dominant features that are most efficient to classify two groups automatically from the feature pool. The effective 35 features were found and used as inputs of artificial neural network. The recognition rate of ANN fed by these input was 81.9% for untrained data set. These results suggest that the combined GA/ANN approach may be useful for early detection of AD and that single channel EEG data might be enough to recognize AD. This approach could be extended to a reliable classification system using EEG recording that can discriminate between groups.