Deep Learning Model for Epileptic Seizure Prediction

Abstract

Prediction of occurrence of a seizure would be of greater help to make necessary precaution for taking care of the patient. A Deep learning model, recurrent neural network (RNN), is designed for predicting the upcoming values in the EEG values. A deep data analysis is made to find the parameter that could best differentiate the normal values and seizure values. Next a recurrent neural network model is built for predicting the values earlier. Four different variants of recurrent neural networks are designed in terms of number of time stamps and the number of LSTM layers and the best model is identified. The best identified RNN model is used for predicting the values. The performance of the model is evaluated in terms of explained variance score and R2 score. The model founds to perform well number of elements in the test dataset is minimal and so this model can predict the seizure values only a few seconds earlier.