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

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 elec...

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Autores principales: Huang, Yueyan, Li, Qingfeng, Yang, Qian, Huang, Zhijing, Gao, Hongbo, Xu, Yunan, Liao, Lianghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245758/
https://www.ncbi.nlm.nih.gov/pubmed/34220480
http://dx.doi.org/10.3389/fnbot.2021.690220
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author Huang, Yueyan
Li, Qingfeng
Yang, Qian
Huang, Zhijing
Gao, Hongbo
Xu, Yunan
Liao, Lianghua
author_facet Huang, Yueyan
Li, Qingfeng
Yang, Qian
Huang, Zhijing
Gao, Hongbo
Xu, Yunan
Liao, Lianghua
author_sort Huang, Yueyan
collection PubMed
description 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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spelling pubmed-82457582021-07-02 Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network Huang, Yueyan Li, Qingfeng Yang, Qian Huang, Zhijing Gao, Hongbo Xu, Yunan Liao, Lianghua Front Neurorobot Neuroscience 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. Frontiers Media S.A. 2021-06-17 /pmc/articles/PMC8245758/ /pubmed/34220480 http://dx.doi.org/10.3389/fnbot.2021.690220 Text en Copyright © 2021 Huang, Li, Yang, Huang, Gao, Xu and Liao. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Neuroscience
Huang, Yueyan
Li, Qingfeng
Yang, Qian
Huang, Zhijing
Gao, Hongbo
Xu, Yunan
Liao, Lianghua
Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network
title Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network
title_full Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network
title_fullStr Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network
title_full_unstemmed Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network
title_short Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network
title_sort early prediction of refractory epilepsy in children under artificial intelligence neural network
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245758/
https://www.ncbi.nlm.nih.gov/pubmed/34220480
http://dx.doi.org/10.3389/fnbot.2021.690220
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