Detecção Precoce das Doenças de Alzheimer e Parkinson através de Parâmetros Não-Lineares Multibanda de Sinais EEG

—This work aims to detect Alzheimer’s and Parkinson’s diseases at early stage through non-linear multiband parameters of EEG signals. For each pair of study groups, parameters selection was performed through genetic algorithm. The selected parameters are used as input for classifiers with leave-one-out cross-validation. Classification accuracies of 100% are achieved, in at least one subband, for 3 pairs of study groups while 90.60% is achieved for the Control vs Alzheimer/Parkinson pair. The delta subband showed, in general, the greatest significant differences between the groups.