DISCRIMINATION BETWEEN DEMENTED PATIENTS AND NORMALS BASED ON TOPOGRAPHIC EEG SLOW-WAVE ACTIVITY - COMPARISON BETWEEN Z-STATISTICS, DISCRIMINANT-ANALYSIS AND ARTIFICIAL NEURAL-NETWORK CLASSIFIERS

The topographic distributions of absolute delta and theta powers were used to classify demented patients and normals by means of z statistics, discriminant analysis and artificial neural networks (NN). The data were taken from two psychopharmacological studies in mildly to moderately demented patients (111 and 96 patients for studies I and II, respectively) and from 56 normal healthy controls. All patients were diagnosed according to DSM-III criteria and were free of medication for at least 2 weeks. The NN used was a strictly layered feed-forward network with complete connections. The z-transformed absolute power values in the combined delta and theta frequency range at 17 electrodes, recorded in a 3 min vigilance-controlled EEG with eyes closed, were used as input. After having trained the NN successfully by backpropagating of errors, the generalization test with independent data resulted in a classification performance of 90% determined by ''relative operating characteristic'' analysis. The NN out-performed z statistics and discriminant analysis. This high percentage of correct classifications may justify the development of further application of NNs based on topographic EEG data.