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Study Epileptic EEG Signals Based on Phase Transition Ideas

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DOI: 10.23977/icamcs.2017.1010


Li Zhangyong, Zuo Jing, Wan Xiaoqin, Wang Wei, Zeng Chen

Corresponding Author

Zeng Chen


Objective: Signals are disorganized during epileptic seizures, and analysis of epileptic EEG signals is difficult and complex.The purpose is to study the epileptic EEG signal and the space-time evolution of brain electrical activity based on phase transition ideas.Methods: electrode arrays was inplantied to the dog’s brain,and installing a telemetry device on the dog.Then acquired the signal at 400Hz by 16 channels.The data is divided into interictal and preictal. Firstly, classify the data with the classifier, observe the classification effect, select the best segment range.Then we use the phase transition idea and correlation to analysis the interictal and preictal data. Results:The correlation coefficient of signal channels is increasing with the passage of time. SVM classifier can be used to achieve the best classification of the signal, the AUC can be as high as 0.93 or more.When there are many channels connected, the signal will fluctuate abnormally.Conclusion: When the connectivity condition continues, the signal continues to be abnormal, imminent epilepsy. The final results is that the correct rate is 80.00%.The method proposed in this paper can be used to analyze the signal of dog 1, the correct rate is very high,and the overall correct rate is Combinating the phase change and epilepsy EEG to study the epileptic EEG signal from a new perspective has a certain reference value for the prediction of epilepsy.


epileptic EEG, correlation, phase transition, SVM

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