Abstract
BackgroundEpilepsy 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.MethodsIn 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.ResultsOur 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.ConclusionIn 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.
Highlights
Epilepsy was defined as an abnormal brain network model disease in the latest definition
Combined with the patients’clinical diagnosis reports, we found that patient with temporal lobe epilepsy were more active in T5, O1, T4, F3, and F7, and patient with frontal lobe epilepsy were more active in F4, F3, C4, and C3
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
Summary
Epilepsy was defined as an abnormal brain network model disease in the latest definition. It is important to observe the Mutual Information (MI) of the whole brain network based on different lead positions. The spatial network connected by the brain leads contains important information. Ma et al BMC Med Inform Decis Mak 2021, 21(Suppl 2): important to observe MI of the whole brain network based on different lead positions. About the brain network model, the major research points are on the propagation of EEG signals in the brain network model and the information interaction between brain different regions and periods, which is helpful to gain a deeper understanding of the whole process mechanism of the seizure brain network [7]. Brain network is a complex network with connections during static and dynamic brain activities. EEG can record the different brain regions’time series signals, which can reflect the activity and coordination between the brain regions. Most studies only selected two periods for researching, and did not involve more studies on the transition state of epilepsy in more different periods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
