An EEG based Nonlinearity Analysis Method for Schizophrenia Diagnosis

Qinglin Zhao, Bin Hu, Li Liu, Martyn Ratcliffe, Hong Peng, Jingwei Zhai, Lanlan Li, Qiuxia Shi, Quanying Liu, and Yanbing Qi


Biomedical Signal Processing, Biomedical Computing, Nonlinear, Schizophrenia


In this paper, the complexity and chaos of EEG (electroencephalogram) signals exhibited in schizophrenic patients are analyzed using four nonlinear features: C0-complexity, Kolmogorov entropy together with an estimation of the correlation dimension and Lempel-Ziv complexity. The first two of these being novel applications of these measures. EEGs from 31 schizophrenic patients (18 males, 13 females, mean age 25.9±3.6 years) and 31 age/sex matched control subjects were recorded using 12 electrodes. In a t-test, it was found that all four nonlinear features had a significant variance between the schizophrenics and the control set (p ≤ 0.05). A classification accuracy of 91.7% was obtained by Back Propagation Neural Networks. Our results show that the discrimination of schizophrenic behavior is possible with respect to a control set using nonlinear analysis of EEG signals. We also assert that these methods may be the basis for a valuable tool set of EEG methods that could be used by psychiatrists when diagnosing schizophrenic patients.

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