Seizure Detection in Focal Epileptic Patients Based on Adaptive Multi-Scale Brain Functional Connectivity
Xu Jiayang1, Yang Tingting1, Li Wen1, Li Kuo2, Du Changwang2, Liu Xiaofang2, Sheng Duozheng1,3, Yan Xiangguo1, Wang Gang1#*
1(Institute of Health and Rehabilitation Science, School of Life Science and Technology, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi′an Jiaotong University, Xi′an 710049, China) 2(Department of Neurosurgery, First Affiliated Hospital of Xi′an Jiaotong University, Xi′an 710061, China) 3(Beijing Braincare Technology Co. Ltd, Beijing 100071, China)
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.
作者简介: #中国生物医学工程学会会员(Member, Chinese Society of Biomedical Engineering)
引用本文:
徐嘉阳, 杨婷婷, 李雯, 李扩, 杜昌旺, 刘晓芳, 盛多铮, 闫相国, 王刚. 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[J]. 中国生物医学工程学报, 2022, 41(4): 393-401.
Xu Jiayang, Yang Tingting, Li Wen, Li Kuo, Du Changwang, Liu Xiaofang, Sheng Duozheng, Yan Xiangguo, Wang Gang. Seizure Detection in Focal Epileptic Patients Based on Adaptive Multi-Scale Brain Functional Connectivity. Chinese Journal of Biomedical Engineering, 2022, 41(4): 393-401.
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