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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., (© 2021. The Author(s).)

Background: Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients' EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information. Recently, deep learning technology has been widely applied to perform image processing tasks, which could learns useful features from data and process multi-channel data automatically. To provide an effective system for automatic seizure detection, we proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel EEG signals., Methods: EEG data of 13 patients were collected from one center hospital, which has already been inspected by experts. To represent EEG data in CNN, firstly time series of each channel of EEG data was converted into the two-dimensional image. Then all channel images were combined into 3D images according to the mutual correlation intensity between different electrodes. Finally, a CNN was constructed using 3D kernels to predict different stages of EEG data, including inter-ictal, pre-ictal, and ictal stages. The system performance was evaluated and compared with the traditional feature-based classifier and the two-dimensional (2D) deep learning method., Results: It demonstrated that multi-channel EEG data could provide more information for increasing the specificity and sensitivity in cpmparison result between the single and multi-channel. And the 3D CNN based on multi-channel outperformed the 2D CNN and traditional signal processing methods with an accuracy of more than 90%, an sensitivity of 88.90% and an specificity of 93.78%., Conclusions: This is the first effort to apply 3D CNN in detecting seizures from EEG. It provides a new way of learning patterns simultaneously from multi-channel EEG signals, and demonstrates that deep neural networks in combination with 3D kernels can establish an effective system for seizure detection.

Introduction: It has been suggested that uncontrolled epilepsy might not necessarily equate to drug resistance when antiepileptic drugs (AEDs) are used at relatively low doses, a practice frequently observed in rural areas of China., Objective: To assess the clinical benefits of further drug manipulation in this situation, we prospectively followed up the outcomes of patients with uncontrolled epilepsy while taking relatively low doses of AEDs., Methods: The study included patients aged 16 years and older with uncontrolled epilepsy and who were receiving at least one AED at a dosage below 50% of the World Health Organization (WHO) defined daily dose (DDD) (50% DDD) (Group 1). Patients with drug-resistant epilepsy were included for comparison (Group 2). Both groups were followed-up for at least 2 years. Seizure outcomes after further drug manipulations were recorded at the last follow-up., Results: A total of 197 patients (55.3% male) were included in Group 1 and 32 (46.9% male) in Group 2; their mean duration of follow-up was 28.85 ± 1.90 and 30.91 ± 2.04 months, respectively. At the last follow-up, 16.8% (33/197) of patients in Group 1 had become seizure-free compared with none in Group 2 (p < 0.001). Seventeen of 93 (18.3%) patients in Group 1 became seizure free after increasing the dosage of baseline AED(s) alone. Only 5.5% (3/55) of patients who had failed to respond to an AED at ≥50% DDD at baseline became seizure free compared with 21.1% (30/142) who did not have such a history (p = 0.001). The number of AEDs taken at a dosage below 50% DDD at baseline was not associated with seizure outcome., Conclusions: Uncontrolled epilepsy could become controlled in a substantial proportion of patients by dose increase alone, particularly if there is no history of drug failure at ≥50% DDD.

Introduction: Cerebral cortical ischemia is a risk factor for post-stroke seizures. However, the optimal imaging method is unclear. We investigated CT perfusion (CTP) in detecting cortical ischemia and its correlation with post-stroke seizures compared with non-contrast CT (NCCT). Methods: We included patients with acute ischemic stroke admitted to the Royal Melbourne Hospital between 2009 and 2014. Post-stroke seizure information was collected. Cortical involvement was determined on acute NCCT and CTP ( T, cerebral blood volume [CBV], and cerebral blood flow [CBF]). The association between cortical involvement detected by different imaging modalities and post-stroke seizures was examined. Results: Three-hundred fifty-two patients were included for analysis. Fifty-nine percent were male, and median age was 73 years (inter-quartile range 61-82). Follow-up was available for 96 %; median follow-up duration was 377 days (inter-quartile range 91-1018 days). Thirteen patients had post-stroke seizures (3.9 %). Cortical involvement was significantly associated with post-stroke seizures across all modalities. CBV had the highest hazard ratio (11.3, 95 % confidence interval (CI) 1.1-41.2), followed by NCCT (5.3, 95 % CI 1.5-18.0) and CBF (4.2, 95 % CI 1.1-15.2). Sensitivity was highest for T (100 %), followed by CBV and CBF (both 76.9 %) and NCCT (63.6 %). Specificity was highest for CBV (77.8 %), then NCCT (75.6 %), CBF (54.0 %), and T (29.1 %). Receiver-operating characteristic area under the curve was significantly different between imaging modalities ( p < 0.001), CBV 0.77, NCCT 0.70, CBF 0.65, and T 0.65. Conclusion: CTP may improve sensitivity and specificity of cortical involvement for post-stroke seizures compared to NCCT. [ABSTRACT FROM AUTHOR]