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Epilepsy is a common neurological disease, incidence of a disease is higher, it is mainly caused by excessive discharge of brain neurons, attack, the characteristics of transient, repeatability and consistency, in recent years the rise in the number of global disease, epilepsy, serious damage to human disease, so the research of epileptic seizure prediction is very necessary.In the traditional epilepsy prediction framework, due to the traditional convolutional neural network (CNN) network model and long and short-term memory network LSTM network hidden layer units and learning rate will be set up improper overfit and instability, will lead to poor network prediction effect. Therefore, the combination with CNN network and LSTM network is selected as the classification model in seizure prediction to form the epilepsy prediction model. The main research content of this paper are as follows: before extracting the seizure EEG data, using wavelet transform to preprocess EEG signals, and select sample entropy as a feature extraction method to predict the processed data using CNN-LSTM, the prediction accuracy reached 87.8%, 4.3% over CNN and 5.0% over LSTM.It shows that the present method can solve the classification problem of existing epilepsy datasets well. © 2022 IEEE.
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年份: 2022
页码: 131-135
语种: 英文
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