Mengnan Ma1,2, Xiaoyan Wei3, Yinlin Cheng1,2, Ziyi Chen4, Yi Zhou5,6. 1. School of Biomedical Engineering, Sun Yat-sen University, No. 132 Waihuan East Road, Guangzhou, 510006, China. 2. Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No. 74 Zhongshan 2nd Road, Guangzhou, 510080, China. 3. Minister of Science, Education and Data Management Department, Guangzhou Women and Children's Medical Center, National Children's Medical Center for South Central Region, Guangzhou Medical University, No. 9 Jinsui Road, Guangzhou, 510623, China. 4. Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan 2nd Road, Guangzhou, 510080, China. 5. Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No. 74 Zhongshan 2nd Road, Guangzhou, 510080, China. zhouyi@mail.sysu.edu.cn. 6. Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, No. 74 Zhongshan 2nd Road, Guangzhou, 510080, China. zhouyi@mail.sysu.edu.cn.
Abstract
BACKGROUND: Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions. METHODS: In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network. RESULTS: Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly. CONCLUSION: In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.
BACKGROUND:Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions. METHODS: In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsypatients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network. RESULTS: Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly. CONCLUSION: In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.
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