基于自适应多尺度脑功能连接的局灶性癫痫发作检测方法研究

doi:10.3969/j.issn.0258-8021.2022.04.002

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Abstract Long-term EEG has been widely used to detect epileptic seizures in clinical practice. However, this approach is tedious and time-consuming, and largely depends on clinicians′ experience and subjective judgment. As a result, the accuracy and repeatability of the manual detection results are low. In this study, with the aim of solving the problem by using long-term EEG to monitor epileptic seizures, we proposed a novel adaptive and multiscale brain functional connectivity (AMBFC) method for epilepsy detection. Samples of 10 epilepsy patients during the seizure and non-seizure periods were selected as the research subjects. First, within a sliding time window, seven IMF components and residuals of the 19-channel EEG signal were extracted by MEMD. Then MVAR model was established to extract the outflow information intensity by the directional transfer function, and the features were combined and dimensionally reduced using PCA. Finally, the CSVM model was used to classify the seizure phase and non-seizure phase EEG, and the effect of epileptic seizures was evaluated through five-fold cross-validation. The results showed that the average accuracy rate of AMBFC algorithm for detecting EEG seizures was 98.6%, accuracy rate was 81.9%, recall rate was 81.4%, and F-measure value was 0.80. Compared with the detection results of the different IMF components, DTF-CSVM algorithm and methods in recent literatures, AMBFC algorithm was better. Except for the high accuracy, the proposed algorithm also achieved high precision, recall and F2 value. In conclusion, this method can be applied to real-time monitoring of long-term EEG.

Key words： electroencephalogram signal epileptic seizures multivariate empirical mode decomposition(MEMD) directed transfer function(DTF) cost-sensitive support vector machine (CSVM)

Received: 15 January 2021

PACS:

R318

Corresponding Authors: ^*E-mail:ggwang@xjtu.edu.cn

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	Articles by authors
	Xu Jiayang
	Yang Tingting
	Li Wen
	Li Kuo
	Du Changwang
	Liu Xiaofang
	Sheng Duozheng
	Yan Xiangguo
	Wang Gang

Cite this article:

Xu Jiayang,Yang Tingting,Li Wen, et al. Seizure Detection in Focal Epileptic Patients Based on Adaptive Multi-Scale Brain Functional Connectivity[J]. Chinese Journal of Biomedical Engineering, 2022, 41(4): 393-401.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2022.04.002 OR http://cjbme.csbme.org/EN/Y2022/V41/I4/393

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