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Extraction and Analysis of Independent Components in Interictal and Preictal Data

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DOI: 10.23977/icamcs.2017.1009


Li Zhangyong, Wan Xiaoqin, Zuo Jing, Wang Wei, Zheng Bin

Corresponding Author

Li Zhangyong


Epileptic seizures can cause changes in the composition of the brain. Therefore, the analysis of independent signals in EEG with epilepsy can help to understand the development of epilepsy. Every interictal and preictal EEG data which has a lasting acquisition time of 10 minutes is divided into segments at the time interval T=20s, 30s, and 60s respectively.These segments are analyzed by FastICA after the preprocessing of wavelet transform, then the number of the corresponding data segments’ independent components can be counted. Accordingly, the average number of independent components of the whole corresponding data can be obtained. By statistical analysis, it can be known that the number of independent components in preictal data is higher than that in interictal data. And in the preictal data of each period, the closer the data is to the seizure onset, the more the number of independent components will be.Under the condition of the same interictal and preictal data.The difference of the average number of independent components between the preictal and interictal data is the most obvious under the conditions of the same data when T=30s,which is 0~0.7 larger than that when T=20s and 0.2~0.8 larger than that when T=60s.This study can provide a foundation for exploring the epilepsy.


epilepsy,time interval,independent component analysis,statistical analysis

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