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      Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network

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          Abstract

          In order to realize the early prediction of refractory epilepsy in children, data preprocessing technology was used to improve the data quality, and the detection model of refractory epilepsy in children based on convolutional neural network (CNN) was established. Then, the data in the epilepsy electroencephalography (EEG) signal public data set was used for model training and the diagnosis of refractory epilepsy in children. Moreover, back propagation neural network (BPNN), support vector machine (SVM), XGBoost, gradient boosting decision tree (GBDT), AdaBoost algorithm were introduced for comparison. The results showed that the early prediction accuracy of BP, SVM, XGBoost, GBDT, AdaBoost, and the algorithm in this study for refractory epilepsy in children were 0.745, 0.778, 0.885, 0.846, 0.874, and 0.941, respectively. The sensitivities were 0.81, 0.826, 0.822, 0.84, 0.859, and 0.918, respectively. The specificities were 0.683, 0.696, 0.743, 0.792, 0.84, and 0.905, respectively. The accuracy was 0.707, 0.732, 0.765, 0.802, 0.839, and 0.881, respectively. The recall rates were 0.69, 0.716, 0.753, 0.784, 0.813, and 0.877, respectively. F1 scores were 0.698, 0.724, 0.759, 0.793, 0.826, and 0.879, respectively. Through the comparisons of the above six indicators, the algorithm proposed in this study was significantly higher than other algorithms, suggesting that the proposed algorithm was more accurate in early prediction of refractory epilepsy in children. Analysis of the EEG characteristics and magnetic resonance imaging (MRI) images of refractory epilepsy in children suggested that the MRI images of patients' brains under this algorithm had obvious characteristics. The reason for the prediction error of the algorithm was that the duration of epilepsy was too short or the EEG of the patient didn't change notably during the epileptic seizure. In summary, the prediction method of refractory epilepsy in children based on CNN was accurate, which had broad adoption prospects in assisting clinicians in the examination and diagnosis of refractory epilepsy in children.

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          New-onset refractory status epilepticus (NORSE) and febrile infection-related epilepsy syndrome (FIRES): State of the art and perspectives

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            Recent advances in epilepsy

            This paper reviews advances in epilepsy in recent years with an emphasis on therapeutics and underlying mechanisms, including status epilepticus, drug and surgical treatments. Lessons from rarer epilepsies regarding the relationship between epilepsy type, mechanisms and choice of antiepileptic drugs (AED) are explored and data regarding AED use in pregnancy are reviewed. Concepts evolving towards a move from treating seizures to treating epilepsy are discussed, both in terms of the mechanisms of epileptogenesis, and in terms of epilepsy’s broader comorbidity, especially depression.
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              Vagus nerve stimulation (VNS) therapy update

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                Author and article information

                Contributors
                Journal
                Front Neurorobot
                Front Neurorobot
                Front. Neurorobot.
                Frontiers in Neurorobotics
                Frontiers Media S.A.
                1662-5218
                17 June 2021
                2021
                : 15
                : 690220
                Affiliations
                [1] 1Department of Pediatrics, Affiliated Hospital of Youjiang Medical College for Nationalities , Baise, China
                [2] 2Department of Radiology, Affiliated Hospital of Youjiang Medical College for Nationalities , Baise, China
                [3] 3Center for Diagnosis and Research of Pathological Diseases, Affiliated Hospital of Youjiang Medical College for Nationalities , Baise, China
                Author notes

                Edited by: Patrick Siarry, Universite Paris 12, France

                Reviewed by: Yannis Oannidis, Technical University of Crete, Greece; Xudong Miao, Zhejiang Chinese Medical University, China; Winni Smith, University of Warmia and Mazury in Olsztyn, Poland

                *Correspondence: Qingfeng Li liqingfengqf@ 123456yeah.net

                †These authors have contributed equally to this work

                Article
                10.3389/fnbot.2021.690220
                8245758
                34220480
                58f5b0b7-8167-4670-89c1-8f46061104d1
                Copyright © 2021 Huang, Li, Yang, Huang, Gao, Xu and Liao.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 April 2021
                : 18 May 2021
                Page count
                Figures: 5, Tables: 0, Equations: 10, References: 21, Pages: 9, Words: 4742
                Categories
                Neuroscience
                Original Research

                Robotics
                convolutional neural network,refractory epilepsy in children,eeg,mri,disease prediction
                Robotics
                convolutional neural network, refractory epilepsy in children, eeg, mri, disease prediction

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