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 (Frontiers in Neurorobotics, 2021)
In order to realize the early prediction of refractory epilepsy in children, data preprocessing technology was adopted to improve data quality, and then convolutional neural network (CNN) was employed to perform predictions. The results showed that the early prediction accuracy of this algorithm for refractory epilepsy in children was 0.941, the sensitivity was 0.918, the specificity was 0.905, the precision was 0.881, the recall was 0.877, and the F1 score was 0.879. The above six indicators were remarkably superior to those of other algorithms, suggesting that the early prediction of refractory epilepsy in children under this algorithm was accurate. Analysis of the electroencephalography (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.