An Alpha resting EEG study on nonlinear dynamic analysis for schizophrenia

Schizophrenia is a mental disorder that may include delusions, loss of personality, confusion, social withdrawal, psychosis, and bizarre behavior. In this study, we use Electroencephalogram (EEG) signals of the Alpha band to detect the differences between nonlinear EEG features of schizophrenic patients and non-psychiatric controls. EEG signals from 31 schizophrenic patients and 31 age/sex matched normal controls are recorded using 16 electrodes. We calculate permutation entropy, Kolmogorov entropy, the correlation dimension, spectral entropy and the results indicate that the EEG signals from schizophrenics are more complex and irregular than those from normal controls. We compare three feature classifiers (k-Nearest Neighbor, Support Vector Machine and Back-Propagation Neural Network). A feature selection method based on Fisher criterion is used for enhancing the performance of classifiers. The optimal accuracy rate comes from Back-Propagation Neural Network, which is 86.1%. We think that the statistic and classification results make our approach helpful for schizophrenia diagnosis.