Abstract

In order to understand the basic mechanism of brain diseases, autoregressive model coefficients, frequency band energy, approximate entropy, and Lempel-Ziv complexity of magnetoencephalographic signals of schizophrenia were extracted as features. Distance criterion and Plus-L Minus-R algorithm were used to screen channels, back propagation neural network and support vector machine were applied to distinguish magnetoencephalographic signals of schizophrenic and normal people, and genetic algorithm was adopted to select features with significant differences. Finally, electroencephalography experiments were designed based on steady-state visual evoked potentials, and electroencephalographic signals of multiple subjects were collected. The collected signals were analyzed using discrete Fourier transform, canonical correlation analysis and multivariable synchronization index analysis methods. The results showed that the correct classification rates of schizophrenic and normal people were 96.25 and 98.75 %, respectively. The correct classification rates of back propagation neural network and support vector machine were 98.5 and 99.75 %, respectively. Signal energy, correlation coefficient and synchronization index were the highest at the target stimulus frequency. The in-depth study of brain signals in patients with schizophrenia provided a reference for clinical diagnosis.