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This work aims to detect the Alzheimer’s disease at different stages using EEG signals. Powers related to conventional frequencies are obtained from the maximum, minimum and average values of Wavelet Packet power spectrum estimates. For each pair of study groups and electrodes, a parameter selection is performed. The selected parameters are used as input for classifiers with leave-one-out cross-validation. Classification accuracies of 100% are obtained, in at least 1 electrode, for the 6 pairs of groups analyzed, indicating the regions in the scalp with the greatest differences as the disease progresses. Average classification accuracies, including all electrodes, between 81.3 and 91.4% are achieved. Keywords— Alzheimer disease, EEG, power spectrum, subband, wavelet packet.

Detecção da Doença de Alzheimer em Diferentes Estágios através do Espectro de Potências da Wavelet Packet de Sinais EEG