Epileptic Seizure Prediction by Graph Convolutional Network Based on Graph Pooling of Attention
Timely and accurate prediction of epileptic seizures can take intervention measures to prevent from accidental injury be-fore epileptic seizures.In order to improve the accuracy of epileptic seizure prediction,a graph convolution neural network model,based on graph pooling of attention,is proposed for epileptic seizure prediction.The multi-lead Electroencephalography(EEG)data were converted into a graph structure,and an improved graph convolution neural network model was designed.By adding graph pooling of attention,important node information was screened to avoid feature redundancy and improve the learning ability and ro-bustness of the model.On this basis,the effects of different EEG rhythm,sliding time window and prediction duration on epilepsy prediction were analyzed.The simulation results show that the accuracy,recall rate,specificity and F1 value of the prediction model can achieve separately 97.03%,95.89%,98.16%and 96.12%for 5 minutes before seizures,by using the sliding step size of 0.5 s in the 4 s time window.Therefore,this model can improve the prediction accuracy of epileptic seizure and can be easily generalized.
prediction of epileptic seizureEEG signalgraph convolution neural networkgraph pooling of attention
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